{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fafca9f7",
   "metadata": {},
   "source": [
    "# MSRE depletion with fission products removal rates\n",
    "We will run a depletion simuation of the MSRE, introducing continuous removal rates to model fission products removal. \n",
    "\n",
    "**Note:** to run the following script this [branch](https://github.com/openmsr/openmc/tree/msr_13.2_cont) of OpenMC is needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "079fc782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import openmc\n",
    "import openmc.deplete\n",
    "import numpy as np\n",
    "from math import log10\n",
    "import matplotlib.pyplot as plt\n",
    "import os \n",
    "\n",
    "os.system('rm *.xml *.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfca4d07",
   "metadata": {},
   "source": [
    "Let's start by defining materials and geometry in the same fashion of the other notebooks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4bc6b094",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define materials \n",
    "hot_temp = 638.3 + 273.15 # in K\n",
    "salt_density = 2.3275 # g/cm3\n",
    "# Fuel salt \n",
    "salt = openmc.Material(name=\"salt\", temperature = hot_temp)\n",
    "salt.add_nuclide('Li6',1.31480070E-05)\n",
    "salt.add_nuclide('Li7', 0.262960140146177)\n",
    "salt.add_nuclide('Be9',1.1863E-01)\n",
    "salt.add_nuclide('Zr90',1.0543E-02)\n",
    "salt.add_nuclide('Zr91',2.2991E-03)\n",
    "salt.add_nuclide('Zr92',3.5142E-03)\n",
    "salt.add_nuclide('Zr94',3.5613E-03)\n",
    "salt.add_nuclide('Zr96',5.7375E-04)\n",
    "salt.add_nuclide('Hf174',8.3786E-10)\n",
    "salt.add_nuclide('Hf176',2.7545E-08)\n",
    "salt.add_nuclide('Hf177',9.7401E-08)\n",
    "salt.add_nuclide('Hf178',1.4285E-07)\n",
    "salt.add_nuclide('Hf179',7.1323E-08)\n",
    "salt.add_nuclide('Hf180',1.8370E-07)\n",
    "salt.add_nuclide('U234',1.034276246E-05)\n",
    "salt.add_nuclide('U235',1.009695816E-03)\n",
    "salt.add_nuclide('U236',4.227809892E-06)\n",
    "salt.add_nuclide('U238',2.168267822E-03)\n",
    "salt.add_nuclide('Fe54',2.8551E-06)\n",
    "salt.add_nuclide('Fe56',4.4818E-05)\n",
    "salt.add_nuclide('Fe57',1.0350E-06)\n",
    "salt.add_nuclide('Fe58',1.3775E-07)\n",
    "salt.add_nuclide('Cr50',2.1224E-06)\n",
    "salt.add_nuclide('Cr52',4.0928E-05)\n",
    "salt.add_nuclide('Cr53',4.6409E-06)\n",
    "salt.add_nuclide('Cr54',1.1552E-06)\n",
    "salt.add_nuclide('Ni58',5.8597E-06)\n",
    "salt.add_nuclide('Ni60',2.2571E-06)\n",
    "salt.add_nuclide('Ni61',9.8117E-08)\n",
    "salt.add_nuclide('Ni62',3.1284E-07)\n",
    "salt.add_nuclide('Ni64',7.9671E-08)\n",
    "salt.add_nuclide('O16',5.1437E-04)\n",
    "salt.add_nuclide('O17',1.8927E-07)\n",
    "salt.add_nuclide('O18',9.6440E-07)\n",
    "salt.add_nuclide('F19',5.9409E-01)\n",
    "salt.set_density('g/cm3', salt_density)\n",
    "\n",
    "#Moderator graphite block\n",
    "graphite = openmc.Material(name='graphite', temperature = hot_temp)\n",
    "graphite.set_density('g/cm3',1.86)\n",
    "graphite.add_nuclide('C12',1)\n",
    "graphite.add_s_alpha_beta('c_Graphite')\n",
    "\n",
    "#inor-8\n",
    "inor = openmc.Material(name='inor-8', temperature = hot_temp)\n",
    "inor.set_density('g/cm3',8.7745)\n",
    "inor.add_element('Ni',68.5,'wo')\n",
    "inor.add_element('Mo',16.5,'wo')\n",
    "inor.add_element('Cr',7,'wo')\n",
    "inor.add_element('Fe',5,'wo')\n",
    "inor.add_element('C',0.06,'wo')\n",
    "inor.add_element('Al',0.25,'wo')\n",
    "inor.add_element('Ti',0.25,'wo')\n",
    "inor.add_element('S',0.02,'wo')\n",
    "inor.add_element('Mn',1.0,'wo')\n",
    "inor.add_element('Si',1.0,'wo')\n",
    "inor.add_element('Cu',0.35,'wo')\n",
    "inor.add_element('B',0.010,'wo')\n",
    "inor.add_element('W',0.5,'wo')\n",
    "inor.add_element('P',0.015,'wo')\n",
    "inor.add_element('Co',0.2,'wo')\n",
    "\n",
    "#helium\n",
    "helium = openmc.Material(name='helium')\n",
    "helium.add_element('He',1.0)\n",
    "helium.set_density('g/cm3',1.03*(10**-4))\n",
    "\n",
    "#Control rods inconel clad\n",
    "trace = 0.01\n",
    "inconel = openmc.Material(name='inconel', temperature = 65.6 + 273.15)\n",
    "inconel.add_element('Ni',78.5,percent_type='wo')\n",
    "inconel.add_element('Cr',14.0,percent_type='wo')\n",
    "inconel.add_element('Fe',6.5,percent_type='wo')\n",
    "inconel.add_element('Mn',0.25,percent_type='wo')\n",
    "inconel.add_element('Si',0.25,percent_type='wo')\n",
    "inconel.add_element('Cu',0.2,percent_type='wo')\n",
    "inconel.add_element('Co',0.2,percent_type='wo')\n",
    "inconel.add_element('Al',0.2,percent_type='wo')\n",
    "inconel.add_element('Ti',0.2,percent_type='wo')\n",
    "inconel.add_element('Ta',0.5,percent_type='wo')\n",
    "inconel.add_element('W',0.5,percent_type='wo')\n",
    "inconel.add_element('Zn',0.2,percent_type='wo')\n",
    "inconel.add_element('Zr',0.1,percent_type='wo')\n",
    "inconel.add_element('C',trace,percent_type='wo')\n",
    "inconel.add_element('Mo',trace,percent_type='wo')\n",
    "inconel.add_element('Ag',trace,percent_type='wo')\n",
    "inconel.add_element('B',trace,percent_type='wo')\n",
    "inconel.add_element('Ba',trace,percent_type='wo')\n",
    "inconel.add_element('Be',trace,percent_type='wo')\n",
    "inconel.add_element('Ca',trace,percent_type='wo')\n",
    "inconel.add_element('Cd',trace,percent_type='wo')\n",
    "inconel.add_element('V',trace,percent_type='wo')\n",
    "inconel.add_element('Sn',trace,percent_type='wo')\n",
    "inconel.add_element('Mg',trace,percent_type='wo')\n",
    "inconel.set_density('g/cm3',8.5)\n",
    "\n",
    "# SS316 control rod flexible hose\n",
    "ss316 = openmc.Material(name='ss316', temperature = 65.6 + 273.15)\n",
    "ss316.add_element('C',0.026,'wo')\n",
    "ss316.add_element('Si',0.37,'wo')\n",
    "ss316.add_element('Mn',0.16,'wo')\n",
    "ss316.add_element('Cr',16.55,'wo')\n",
    "ss316.add_element('Cu',0.16,'wo')\n",
    "ss316.add_element('Ni',10,'wo')\n",
    "ss316.add_element('P',0.029,'wo')\n",
    "ss316.add_element('S',0.027,'wo')\n",
    "ss316.add_element('Mo',2.02,'wo')\n",
    "ss316.add_element('N',0.036,'wo')\n",
    "ss316.add_element('Fe',70.622,'wo')\n",
    "ss316.set_density('g/cm3',7.99)\n",
    "\n",
    "#Control rods bushing posion material\n",
    "Gd2O3 = openmc.Material()\n",
    "Gd2O3.add_element('Gd',2)\n",
    "Gd2O3.add_element('O',3)\n",
    "Gd2O3.set_density('g/cm3',7.41)\n",
    "Al2O3 = openmc.Material()\n",
    "Al2O3.add_element('Al',2)\n",
    "Al2O3.add_element('O',3)\n",
    "Al2O3.set_density('g/cm3',3.95)\n",
    "bush = openmc.Material.mix_materials([Gd2O3,Al2O3],[0.7,0.3],'wo')\n",
    "bush.name='bush'\n",
    "bush.temperature = 65.6 +273.15\n",
    "\n",
    "#Concrete block\n",
    "concrete = openmc.Material(name='concrete')\n",
    "concrete.add_element('H',0.005,'wo')\n",
    "concrete.add_element('O',0.496,'wo')\n",
    "concrete.add_element('Si',0.314,'wo')\n",
    "concrete.add_element('Ca',0.083,'wo')\n",
    "concrete.add_element('Na',0.017,'wo')\n",
    "concrete.add_element('Mn',0.002,'wo')\n",
    "concrete.add_element('Al',0.046,'wo')\n",
    "concrete.add_element('S',0.001,'wo')\n",
    "concrete.add_element('K',0.019,'wo')\n",
    "concrete.add_element('Fe',0.012,'wo')\n",
    "concrete.set_density('g/cm3',2.35)\n",
    "\n",
    "#Thermal shielding as material mix of water and SS304 50-50 vo\n",
    "water = openmc.Material()\n",
    "water.add_element('H',2)\n",
    "water.add_element('O',1)\n",
    "water.set_density('g/cm3',0.997)\n",
    "\n",
    "#stainless steel 304\n",
    "ss304 =  openmc.Material()\n",
    "ss304.add_element('C',0.08,'wo')\n",
    "ss304.add_element('Mn',2,'wo')\n",
    "ss304.add_element('P',0.045,'wo')\n",
    "ss304.add_element('S',0.03,'wo')\n",
    "ss304.add_element('Si',0.75,'wo')\n",
    "ss304.add_element('Cr',19,'wo')\n",
    "ss304.add_element('Ni',10,'wo')\n",
    "ss304.add_element('N',0.1,'wo')\n",
    "ss304.add_element('Fe',67.995, 'wo')\n",
    "ss304.set_density('g/cm3',7.93)\n",
    "shield = openmc.Material.mix_materials([water,ss304],[0.5,0.5],'vo')\n",
    "shield.temperature = 32.2 + 273.15\n",
    "shield.name='steelwater'\n",
    "\n",
    "# \"Careytemp 1600\" by Philip Carey Manufacturing Compamy (Cincinnati) \n",
    "insulation=openmc.Material(name='insulation')\n",
    "insulation.add_element('Si',1)\n",
    "insulation.add_element('O',2)\n",
    "insulation.set_density('g/cm3',0.16) \n",
    "\n",
    "# Sand water (not sure about this material)\n",
    "sandwater=openmc.Material(name='sandwater')\n",
    "sandwater.add_element('Fe',3)\n",
    "sandwater.add_element('O',4)\n",
    "sandwater.set_density('g/cm3',6)\n",
    "\n",
    "#Vessel anular steel\n",
    "steel = openmc.Material(name='steel')\n",
    "steel.add_element('Fe',1)\n",
    "steel.set_density('g/cm3',7.85)\n",
    "\n",
    "mats = openmc.Materials([salt,graphite,inor,helium,inconel,shield,concrete,steel,ss316,sandwater,insulation,bush])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "edfa5bd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import h5m files\n",
    "core_h5m = '../h5m/msre_reactor_1e-2.h5m'\n",
    "cr_h5m = '../h5m/msre_control_rod_1e-2.h5m'\n",
    "\n",
    "# Create DAGMC universes out of h5m files\n",
    "core = openmc.DAGMCUniverse(filename=core_h5m, auto_geom_ids=True, universe_id=1)\n",
    "cr = openmc.DAGMCUniverse(filename=cr_h5m, auto_geom_ids=True, universe_id=2)\n",
    "\n",
    "# Create regions\n",
    "core_region = core.bounding_region()\n",
    "cr1_region = cr.bounding_region(boundary_type='transmission', starting_id=20000)\n",
    "\n",
    "# Extend control rod region, to include upwards translations\n",
    "cr1_region = cr1_region | cr1_region.translate([0,0,150])\n",
    "\n",
    "# Create control rod region 2 and 3 as translated region of control rod 1\n",
    "offset = 10.163255\n",
    "cr2_region = cr1_region.translate([-offset,0,0])\n",
    "cr3_region = cr1_region.translate([-offset,offset,0])\n",
    "\n",
    "# Create openmc Cells \n",
    "core_cell = openmc.Cell(region=~(cr1_region | cr2_region | cr3_region) & core_region , fill=core)\n",
    "cr1_cell = openmc.Cell(name='CR1', region=cr1_region, fill=cr)\n",
    "cr2_cell = openmc.Cell(name='CR2', region=cr2_region, fill=cr)\n",
    "cr3_cell = openmc.Cell(name='CR3', region=cr3_region, fill=cr)\n",
    "    \n",
    "#translate control rods at top position (fully withdrawn)\n",
    "inch_to_cm = 2.54\n",
    "start_pos = 19.2 #inches\n",
    "top_pos = 51 #inches\n",
    "setattr(cr1_cell, 'translation', [0, 0, start_pos + top_pos*inch_to_cm])\n",
    "setattr(cr2_cell, 'translation', [-offset, 0, start_pos + top_pos*2.54])\n",
    "setattr(cr3_cell, 'translation', [-offset, offset, start_pos + top_pos*2.54])\n",
    "\n",
    "# Create openmc Geometry object\n",
    "geometry = openmc.Geometry([core_cell,cr1_cell,cr2_cell,cr3_cell])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ba99fb4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "settings = openmc.Settings()\n",
    "settings.temperature = {'method':'interpolation','range':(293.15,923.15)}\n",
    "settings.batches = 50\n",
    "settings.inactive = 20\n",
    "settings.particles = 30000\n",
    "settings.photon_transport = False\n",
    "source_area = openmc.stats.Box([-100., -100., 0.],[ 100.,  100.,  200.],only_fissionable = True)\n",
    "settings.source = openmc.Source(space=source_area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16520ed",
   "metadata": {},
   "source": [
    "First of all, we will set up and run a standard depletion analysis that will be used as the reference to compare with the next case.\n",
    "\n",
    "Let's define some general settings (timestep, power, depletable volume, integration scheme) and run the analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b9f8eada",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
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      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.2\n",
      "        Git SHA1 | 030f73a8690ed19e91806e46c8caf338d252e74a\n",
      "       Date/Time | 2023-01-20 11:06:11\n",
      "   MPI Processes | 1\n",
      "  OpenMP Threads | 56\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file h5m/msre_reactor_1e-2.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 0.01\n",
      "Building acceleration data structures...\n",
      "Implicit Complement assumed to be Vacuum\n",
      "Using the DOUBLE-DOWN interface to Embree.\n",
      "Loading file h5m/msre_control_rod_1e-2.h5m\n",
      "Initializing the GeomQueryTool...\n",
      "Using faceting tolerance: 0.01\n",
      "Building acceleration data structures...\n",
      "Implicit Complement assumed to be Vacuum\n",
      " Reading Li6 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li6.h5\n",
      " Reading Li7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Li7.h5\n",
      " Reading Be9 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be9.h5\n",
      " Reading O16 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O16.h5\n",
      " Reading O17 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O17.h5\n",
      " Reading O18 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/O18.h5\n",
      " Reading F19 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/F19.h5\n",
      " Reading Cr50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr50.h5\n",
      " Reading Cr52 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr52.h5\n",
      " Reading Cr53 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr53.h5\n",
      " Reading Cr54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr54.h5\n",
      " Reading Fe54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe54.h5\n",
      " Reading Fe56 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe56.h5\n",
      " Reading Fe57 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe57.h5\n",
      " Reading Fe58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe58.h5\n",
      " Reading Ni58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni58.h5\n",
      " Reading Ni60 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni60.h5\n",
      " Reading Ni61 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni61.h5\n",
      " Reading Ni62 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni62.h5\n",
      " Reading Ni64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni64.h5\n",
      " Reading Zr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr90.h5\n",
      " Reading Zr91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr91.h5\n",
      " Reading Zr92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr92.h5\n",
      " Reading Zr94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr94.h5\n",
      " Reading Zr96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr96.h5\n",
      " Reading Hf174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf174.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Zr96 at 1200K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Hf176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf176.h5\n",
      " Reading Hf177 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf177.h5\n",
      " Reading Hf178 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf178.h5\n",
      " Reading Hf179 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf179.h5\n",
      " Reading Hf180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf180.h5\n",
      " Reading U234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U234.h5\n",
      " Reading U235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U235.h5\n",
      " Reading U236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U236.h5\n",
      " Reading U238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U238.h5\n",
      " Reading C12 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C12.h5\n",
      " Reading B10 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B10.h5\n",
      " Reading B11 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/B11.h5\n",
      " Reading C13 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/C13.h5\n",
      " Reading Al27 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al27.h5\n",
      " Reading Si28 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si28.h5\n",
      " Reading Si29 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si29.h5\n",
      " Reading Si30 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si30.h5\n",
      " Reading P31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/P31.h5\n",
      " Reading S32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S32.h5\n",
      " Reading S33 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S33.h5\n",
      " Reading S34 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S34.h5\n",
      " Reading S36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S36.h5\n",
      " Reading Ti46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti46.h5\n",
      " Reading Ti47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti47.h5\n",
      " Reading Ti48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti48.h5\n",
      " Reading Ti49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti49.h5\n",
      " Reading Ti50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ti50.h5\n",
      " Reading Mn55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn55.h5\n",
      " Reading Co59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co59.h5\n",
      " Reading Cu63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu63.h5\n",
      " Reading Cu65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu65.h5\n",
      " Reading Mo92 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo92.h5\n",
      " Reading Mo94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo94.h5\n",
      " Reading Mo95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo95.h5\n",
      " Reading Mo96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo96.h5\n",
      " Reading Mo97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo97.h5\n",
      " Reading Mo98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo98.h5\n",
      " Reading Mo100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo100.h5\n",
      " Reading W180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W180.h5\n",
      " Reading W182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W182.h5\n",
      " Reading W183 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W183.h5\n",
      " Reading W184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W184.h5\n",
      " Reading W186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W186.h5\n",
      " Reading He3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He3.h5\n",
      " Reading He4 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/He4.h5\n",
      " Reading Mg24 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg24.h5\n",
      " Reading Mg25 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg25.h5\n",
      " Reading Mg26 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mg26.h5\n",
      " Reading Ca40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca40.h5\n",
      " Reading Ca42 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca42.h5\n",
      " Reading Ca43 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca43.h5\n",
      " Reading Ca44 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca44.h5\n",
      " Reading Ca46 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca46.h5\n",
      " Reading Ca48 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca48.h5\n",
      " Reading V50 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V50.h5\n",
      " Reading V51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V51.h5\n",
      " Reading Zn64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn64.h5\n",
      " Reading Zn66 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn66.h5\n",
      " Reading Zn67 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn67.h5\n",
      " Reading Zn68 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn68.h5\n",
      " Reading Zn70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn70.h5\n",
      " Reading Ag107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag107.h5\n",
      " Reading Ag109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag109.h5\n",
      " Reading Cd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd106.h5\n",
      " Reading Cd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd108.h5\n",
      " Reading Cd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd110.h5\n",
      " Reading Cd111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd111.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cd106 at\n",
      "          1200K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Cd112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd112.h5\n",
      " Reading Cd113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd113.h5\n",
      " Reading Cd114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd114.h5\n",
      " Reading Cd116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd116.h5\n",
      " Reading Sn112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn112.h5\n",
      " Reading Sn114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn114.h5\n",
      " Reading Sn115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn115.h5\n",
      " Reading Sn116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn116.h5\n",
      " Reading Sn117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn117.h5\n",
      " Reading Sn118 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn118.h5\n",
      " Reading Sn119 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn119.h5\n",
      " Reading Sn120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn120.h5\n",
      " Reading Sn122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn122.h5\n",
      " Reading Sn124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn124.h5\n",
      " Reading Ba130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba130.h5\n",
      " Reading Ba132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba132.h5\n",
      " Reading Ba134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba134.h5\n",
      " Reading Ba135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba135.h5\n",
      " Reading Ba136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba136.h5\n",
      " Reading Ba137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba137.h5\n",
      " Reading Ba138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba138.h5\n",
      " Reading Ta180 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta180.h5\n",
      " Reading Ta181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta181.h5\n",
      " Reading N14 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N14.h5\n",
      " Reading N15 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/N15.h5\n",
      " Reading Gd152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd152.h5\n",
      " Reading Gd154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd154.h5\n",
      " Reading Gd155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd155.h5\n",
      " Reading Gd156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd156.h5\n",
      " Reading Gd157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd157.h5\n",
      " Reading Gd158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd158.h5\n",
      " Reading Gd160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd160.h5\n",
      " Reading H1 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H1.h5\n",
      " Reading H2 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H2.h5\n",
      " Reading Na23 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na23.h5\n",
      " Reading K39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K39.h5\n",
      " Reading K40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K40.h5\n",
      " Reading K41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/K41.h5\n",
      " Reading c_Graphite from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/c_Graphite.h5\n",
      " Minimum neutron data temperature: 250 K\n",
      " Maximum neutron data temperature: 1200 K\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n",
      " Reading H3 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/H3.h5\n",
      " Reading Be7 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Be7.h5\n",
      " Reading Ne20 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ne20.h5\n",
      " Reading Ne21 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ne21.h5\n",
      " Reading Ne22 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ne22.h5\n",
      " Reading Na22 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Na22.h5\n",
      " Reading Al26_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Al26_m1.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Na22 at 2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Si31 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si31.h5\n",
      " Reading Si32 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Si32.h5\n",
      " Reading S35 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/S35.h5\n",
      " Reading Cl35 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cl35.h5\n",
      " Reading Cl36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cl36.h5\n",
      " Reading Cl37 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cl37.h5\n",
      " Reading Ar36 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar36.h5\n",
      " Reading Ar37 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar37.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Ar38 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar38.h5\n",
      " Reading Ar39 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar39.h5\n",
      " Reading Ar40 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar40.h5\n",
      " Reading Ar41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ar41.h5\n",
      " Reading Ca41 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca41.h5\n",
      " Reading Ca45 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca45.h5\n",
      " Reading Ca47 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ca47.h5\n",
      " Reading Sc45 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sc45.h5\n",
      " Reading V49 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/V49.h5\n",
      " Reading Cr51 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cr51.h5\n",
      " Reading Mn54 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mn54.h5\n",
      " Reading Fe55 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fe55.h5\n",
      " Reading Co58 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co58.h5\n",
      " Reading Co58_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Co58_m1.h5\n",
      " Reading Ni59 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni59.h5\n",
      " Reading Ni63 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ni63.h5\n",
      " Reading Cu64 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cu64.h5\n",
      " Reading Zn65 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn65.h5\n",
      " Reading Zn69 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zn69.h5\n",
      " Reading Ga69 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ga69.h5\n",
      " Reading Ga70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ga70.h5\n",
      " Reading Ga71 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ga71.h5\n",
      " Reading Ge70 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge70.h5\n",
      " Reading Ge71 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge71.h5\n",
      " Reading Ge72 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge72.h5\n",
      " Reading Ge73 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge73.h5\n",
      " Reading Ge74 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge74.h5\n",
      " Reading Ge75 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge75.h5\n",
      " Reading Ge76 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ge76.h5\n",
      " Reading As73 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/As73.h5\n",
      " Reading As74 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/As74.h5\n",
      " Reading As75 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/As75.h5\n",
      " Reading Se74 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se74.h5\n",
      " Reading Se75 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se75.h5\n",
      " Reading Se76 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se76.h5\n",
      " Reading Se77 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se77.h5\n",
      " Reading Se78 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se78.h5\n",
      " Reading Se79 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se79.h5\n",
      " Reading Se80 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se80.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Se79 at 1200K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Se81 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se81.h5\n",
      " Reading Se82 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Se82.h5\n",
      " Reading Br79 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Br79.h5\n",
      " Reading Br80 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Br80.h5\n",
      " Reading Br81 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Br81.h5\n",
      " Reading Kr78 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr78.h5\n",
      " Reading Kr79 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr79.h5\n",
      " Reading Kr80 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr80.h5\n",
      " Reading Kr81 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr81.h5\n",
      " Reading Kr82 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr82.h5\n",
      " Reading Kr83 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr83.h5\n",
      " Reading Kr84 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr84.h5\n",
      " Reading Kr85 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr85.h5\n",
      " Reading Kr86 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Kr86.h5\n",
      " Reading Rb85 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rb85.h5\n",
      " Reading Rb86 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rb86.h5\n",
      " Reading Rb87 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rb87.h5\n",
      " Reading Sr84 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr84.h5\n",
      " Reading Sr85 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr85.h5\n",
      " Reading Sr86 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr86.h5\n",
      " Reading Sr87 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr87.h5\n",
      " Reading Sr88 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr88.h5\n",
      " Reading Sr89 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr89.h5\n",
      " Reading Sr90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sr90.h5\n",
      " Reading Y89 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Y89.h5\n",
      " Reading Y90 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Y90.h5\n",
      " Reading Y91 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Y91.h5\n",
      " Reading Zr93 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr93.h5\n",
      " Reading Zr95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Zr95.h5\n",
      " Reading Nb93 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nb93.h5\n",
      " Reading Nb94 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nb94.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb94 at 2500K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Nb95 at 1200K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Nb95 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nb95.h5\n",
      " Reading Mo93 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo93.h5\n",
      " Reading Mo99 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Mo99.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Mo99 at 2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Tc98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tc98.h5\n",
      " Reading Tc99 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tc99.h5\n",
      " Reading Ru96 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru96.h5\n",
      " Reading Ru97 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru97.h5\n",
      " Reading Ru98 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru98.h5\n",
      " Reading Ru99 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru99.h5\n",
      " Reading Ru100 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru100.h5\n",
      " Reading Ru101 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru101.h5\n",
      " Reading Ru102 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru102.h5\n",
      " Reading Ru103 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru103.h5\n",
      " Reading Ru104 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru104.h5\n",
      " Reading Ru105 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru105.h5\n",
      " Reading Ru106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ru106.h5\n",
      " Reading Rh103 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rh103.h5\n",
      " Reading Rh104 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rh104.h5\n",
      " Reading Rh105 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Rh105.h5\n",
      " Reading Pd102 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd102.h5\n",
      " Reading Pd103 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd103.h5\n",
      " Reading Pd104 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd104.h5\n",
      " Reading Pd105 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd105.h5\n",
      " Reading Pd106 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd106.h5\n",
      " Reading Pd107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd107.h5\n",
      " Reading Pd108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd108.h5\n",
      " Reading Pd109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd109.h5\n",
      " Reading Pd110 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pd110.h5\n",
      " Reading Ag108 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag108.h5\n",
      " Reading Ag110_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag110_m1.h5\n",
      " Reading Ag111 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag111.h5\n",
      " Reading Ag112 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag112.h5\n",
      " Reading Ag113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag113.h5\n",
      " Reading Ag114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag114.h5\n",
      " Reading Ag115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag115.h5\n",
      " Reading Ag116 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag116.h5\n",
      " Reading Ag117 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag117.h5\n",
      " Reading Ag118_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ag118_m1.h5\n",
      " Reading Cd107 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd107.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
      "          250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
      "          294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
      "          600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
      "          900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ag118_m1 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Cd109 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd109.h5\n",
      " Reading Cd115_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cd115_m1.h5\n",
      " Reading In113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/In113.h5\n",
      " Reading In114 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/In114.h5\n",
      " Reading In115 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/In115.h5\n",
      " Reading Sn113 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn113.h5\n",
      " Reading Sn121_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn121_m1.h5\n",
      " Reading Sn123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn123.h5\n",
      " Reading Sn125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn125.h5\n",
      " Reading Sn126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sn126.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Sn123 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Sb121 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb121.h5\n",
      " Reading Sb122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb122.h5\n",
      " Reading Sb123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb123.h5\n",
      " Reading Sb124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb124.h5\n",
      " Reading Sb125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb125.h5\n",
      " Reading Sb126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sb126.h5\n",
      " Reading Te120 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te120.h5\n",
      " Reading Te121 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te121.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Te120 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Te120 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Te120 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Te121_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te121_m1.h5\n",
      " Reading Te122 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te122.h5\n",
      " Reading Te123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te123.h5\n",
      " Reading Te124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te124.h5\n",
      " Reading Te125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te125.h5\n",
      " Reading Te126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te126.h5\n",
      " Reading Te127_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te127_m1.h5\n",
      " Reading Te128 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te128.h5\n",
      " Reading Te129_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te129_m1.h5\n",
      " Reading Te130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te130.h5\n",
      " Reading Te131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te131.h5\n",
      " Reading Te131_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te131_m1.h5\n",
      " Reading Te132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Te132.h5\n",
      " Reading I127 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I127.h5\n",
      " Reading I128 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I128.h5\n",
      " Reading I129 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I129.h5\n",
      " Reading I130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I130.h5\n",
      " Reading I131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I131.h5\n",
      " Reading I132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I132.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide I131 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide I131 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide I131 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide I131 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide I131 at 1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide I131 at 2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading I132_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I132_m1.h5\n",
      " Reading I133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I133.h5\n",
      " Reading I134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I134.h5\n",
      " Reading I135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/I135.h5\n",
      " Reading Xe123 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe123.h5\n",
      " Reading Xe124 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe124.h5\n",
      " Reading Xe125 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe125.h5\n",
      " Reading Xe126 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe126.h5\n",
      " Reading Xe127 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe127.h5\n",
      " Reading Xe128 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe128.h5\n",
      " Reading Xe129 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe129.h5\n",
      " Reading Xe130 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe130.h5\n",
      " Reading Xe131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe131.h5\n",
      " Reading Xe132 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe132.h5\n",
      " Reading Xe133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe133.h5\n",
      " Reading Xe134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe134.h5\n",
      " Reading Xe135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe135.h5\n",
      " Reading Xe136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Xe136.h5\n",
      " Reading Cs133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs133.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Xe133 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Cs134 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs134.h5\n",
      " Reading Cs135 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs135.h5\n",
      " Reading Cs136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs136.h5\n",
      " Reading Cs137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cs137.h5\n",
      " Reading Ba131 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba131.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cs136 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Ba133 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba133.h5\n",
      " Reading Ba139 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba139.h5\n",
      " Reading Ba140 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ba140.h5\n",
      " Reading La138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/La138.h5\n",
      " Reading La139 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/La139.h5\n",
      " Reading La140 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/La140.h5\n",
      " Reading Ce136 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce136.h5\n",
      " Reading Ce137 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce137.h5\n",
      " Reading Ce137_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce137_m1.h5\n",
      " Reading Ce138 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce138.h5\n",
      " Reading Ce139 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce139.h5\n",
      " Reading Ce140 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce140.h5\n",
      " Reading Ce141 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce141.h5\n",
      " Reading Ce142 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce142.h5\n",
      " Reading Ce143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce143.h5\n",
      " Reading Ce144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ce144.h5\n",
      " Reading Pr141 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pr141.h5\n",
      " Reading Pr142 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pr142.h5\n",
      " Reading Pr143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pr143.h5\n",
      " Reading Nd142 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd142.h5\n",
      " Reading Nd143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd143.h5\n",
      " Reading Nd144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd144.h5\n",
      " Reading Nd145 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd145.h5\n",
      " Reading Nd146 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd146.h5\n",
      " Reading Nd147 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd147.h5\n",
      " Reading Nd148 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd148.h5\n",
      " Reading Nd149 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd149.h5\n",
      " Reading Nd150 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Nd150.h5\n",
      " Reading Pm143 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm143.h5\n",
      " Reading Pm144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm144.h5\n",
      " Reading Pm145 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm145.h5\n",
      " Reading Pm146 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm146.h5\n",
      " Reading Pm147 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm147.h5\n",
      " Reading Pm148 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm148.h5\n",
      " Reading Pm148_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm148_m1.h5\n",
      " Reading Pm149 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm149.h5\n",
      " Reading Pm150 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm150.h5\n",
      " Reading Pm151 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pm151.h5\n",
      " Reading Sm144 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm144.h5\n",
      " Reading Sm145 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm145.h5\n",
      " Reading Sm146 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm146.h5\n",
      " Reading Sm147 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm147.h5\n",
      " Reading Sm148 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm148.h5\n",
      " Reading Sm149 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm149.h5\n",
      " Reading Sm150 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm150.h5\n",
      " Reading Sm151 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm151.h5\n",
      " Reading Sm152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm152.h5\n",
      " Reading Sm153 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm153.h5\n",
      " Reading Sm154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Sm154.h5\n",
      " Reading Eu151 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu151.h5\n",
      " Reading Eu152 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu152.h5\n",
      " Reading Eu153 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu153.h5\n",
      " Reading Eu154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu154.h5\n",
      " Reading Eu155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu155.h5\n",
      " Reading Eu156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu156.h5\n",
      " Reading Eu157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Eu157.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Eu156 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Gd153 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd153.h5\n",
      " Reading Gd159 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Gd159.h5\n",
      " Reading Tb158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb158.h5\n",
      " Reading Tb159 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb159.h5\n",
      " Reading Tb160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb160.h5\n",
      " Reading Tb161 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tb161.h5\n",
      " Reading Dy154 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy154.h5\n",
      " Reading Dy155 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy155.h5\n",
      " Reading Dy156 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy156.h5\n",
      " Reading Dy157 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy157.h5\n",
      " Reading Dy158 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy158.h5\n",
      " Reading Dy159 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy159.h5\n",
      " Reading Dy160 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy160.h5\n",
      " Reading Dy161 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy161.h5\n",
      " Reading Dy162 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy162.h5\n",
      " Reading Dy163 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy163.h5\n",
      " Reading Dy164 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Dy164.h5\n",
      " Reading Ho165 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ho165.h5\n",
      " Reading Ho166_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ho166_m1.h5\n",
      " Reading Er162 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er162.h5\n",
      " Reading Er163 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er163.h5\n",
      " Reading Er164 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er164.h5\n",
      " Reading Er165 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er165.h5\n",
      " Reading Er166 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er166.h5\n",
      " Reading Er167 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er167.h5\n",
      " Reading Er168 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er168.h5\n",
      " Reading Er169 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er169.h5\n",
      " Reading Er170 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Er170.h5\n",
      " Reading Tm168 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm168.h5\n",
      " Reading Tm169 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm169.h5\n",
      " Reading Tm170 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm170.h5\n",
      " Reading Tm171 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tm171.h5\n",
      " Reading Yb168 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb168.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb168 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Yb169 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb169.h5\n",
      " Reading Yb170 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb170.h5\n",
      " Reading Yb171 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb171.h5\n",
      " Reading Yb172 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb172.h5\n",
      " Reading Yb173 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb173.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb170 at\n",
      "          2500K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb171 at\n",
      "          2500K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb172 at\n",
      "          2500K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb173 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Yb174 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb174.h5\n",
      " Reading Yb175 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb175.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb174 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Yb176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Yb176.h5\n",
      " Reading Lu175 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Lu175.h5\n",
      " Reading Lu176 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Lu176.h5\n",
      " Reading Hf175 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf175.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Yb176 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Hf181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf181.h5\n",
      " Reading Hf182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hf182.h5\n",
      " Reading Ta182 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ta182.h5\n",
      " Reading W181 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W181.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf181 at\n",
      "          2500K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 600K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n",
      "          1200K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Hf182 at\n",
      "          2500K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading W185 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/W185.h5\n",
      " Reading Re185 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Re185.h5\n",
      " Reading Re186_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Re186_m1.h5\n",
      " Reading Re187 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Re187.h5\n",
      " Reading Os184 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os184.h5\n",
      " Reading Os185 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os185.h5\n",
      " Reading Os186 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os186.h5\n",
      " Reading Os187 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os187.h5\n",
      " Reading Os188 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os188.h5\n",
      " Reading Os189 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os189.h5\n",
      " Reading Os190 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os190.h5\n",
      " Reading Os191 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os191.h5\n",
      " Reading Os192 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Os192.h5\n",
      " Reading Ir191 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir191.h5\n",
      " Reading Ir192 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir192.h5\n",
      " Reading Ir193 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir193.h5\n",
      " Reading Ir194_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ir194_m1.h5\n",
      " Reading Pt190 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt190.h5\n",
      " Reading Pt191 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt191.h5\n",
      " Reading Pt192 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt192.h5\n",
      " Reading Pt193 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt193.h5\n",
      " Reading Pt194 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt194.h5\n",
      " Reading Pt195 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt195.h5\n",
      " Reading Pt196 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt196.h5\n",
      " Reading Pt197 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt197.h5\n",
      " Reading Pt198 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pt198.h5\n",
      " Reading Au197 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Au197.h5\n",
      " Reading Hg196 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg196.h5\n",
      " Reading Hg197 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg197.h5\n",
      " Reading Hg197_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg197_m1.h5\n",
      " Reading Hg198 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg198.h5\n",
      " Reading Hg199 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg199.h5\n",
      " Reading Hg200 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg200.h5\n",
      " Reading Hg201 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg201.h5\n",
      " Reading Hg202 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg202.h5\n",
      " Reading Hg203 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg203.h5\n",
      " Reading Hg204 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Hg204.h5\n",
      " Reading Tl203 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tl203.h5\n",
      " Reading Tl204 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tl204.h5\n",
      " Reading Tl205 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Tl205.h5\n",
      " Reading Pb204 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb204.h5\n",
      " Reading Pb205 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb205.h5\n",
      " Reading Pb206 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb206.h5\n",
      " Reading Pb207 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb207.h5\n",
      " Reading Pb208 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pb208.h5\n",
      " Reading Bi209 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bi209.h5\n",
      " Reading Bi210_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bi210_m1.h5\n",
      " Reading Po208 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Po208.h5\n",
      " Reading Po209 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Po209.h5\n",
      " Reading Po210 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Po210.h5\n",
      " Reading Ra223 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra223.h5\n",
      " Reading Ra224 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra224.h5\n",
      " Reading Ra225 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra225.h5\n",
      " Reading Ra226 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ra226.h5\n",
      " Reading Ac225 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ac225.h5\n",
      " Reading Ac226 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ac226.h5\n",
      " Reading Ac227 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Ac227.h5\n",
      " Reading Th227 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th227.h5\n",
      " Reading Th228 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th228.h5\n",
      " Reading Th229 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th229.h5\n",
      " Reading Th230 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th230.h5\n",
      " Reading Th231 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th231.h5\n",
      " Reading Th232 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th232.h5\n",
      " Reading Th233 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th233.h5\n",
      " Reading Th234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Th234.h5\n",
      " Reading Pa229 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa229.h5\n",
      " Reading Pa230 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa230.h5\n",
      " Reading Pa231 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa231.h5\n",
      " Reading Pa232 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa232.h5\n",
      " Reading Pa233 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pa233.h5\n",
      " Reading U230 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U230.h5\n",
      " Reading U231 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U231.h5\n",
      " Reading U232 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U232.h5\n",
      " Reading U233 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U233.h5\n",
      " Reading U237 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U237.h5\n",
      " Reading U239 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U239.h5\n",
      " Reading U240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U240.h5\n",
      " Reading U241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/U241.h5\n",
      " Reading Np234 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np234.h5\n",
      " Reading Np235 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np235.h5\n",
      " Reading Np236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np236.h5\n",
      " Reading Np236_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np236_m1.h5\n",
      " Reading Np237 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np237.h5\n",
      " Reading Np238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np238.h5\n",
      " Reading Np239 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Np239.h5\n",
      " Reading Pu236 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu236.h5\n",
      " Reading Pu237 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu237.h5\n",
      " Reading Pu238 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu238.h5\n",
      " Reading Pu239 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu239.h5\n",
      " Reading Pu240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu240.h5\n",
      " Reading Pu241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu241.h5\n",
      " Reading Pu242 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu242.h5\n",
      " Reading Pu243 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu243.h5\n",
      " Reading Pu244 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu244.h5\n",
      " Reading Pu245 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu245.h5\n",
      " Reading Pu246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Pu246.h5\n",
      " Reading Am240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am240.h5\n",
      " Reading Am241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am241.h5\n",
      " Reading Am242 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am242.h5\n",
      " Reading Am242_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am242_m1.h5\n",
      " Reading Am243 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am243.h5\n",
      " Reading Am244 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am244.h5\n",
      " Reading Am244_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Am244_m1.h5\n",
      " Reading Cm240 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm240.h5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Cm241 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm241.h5\n",
      " Reading Cm242 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm242.h5\n",
      " Reading Cm243 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm243.h5\n",
      " Reading Cm244 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm244.h5\n",
      " Reading Cm245 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm245.h5\n",
      " Reading Cm246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm246.h5\n",
      " Reading Cm247 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm247.h5\n",
      " Reading Cm248 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm248.h5\n",
      " Reading Cm249 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm249.h5\n",
      " Reading Cm250 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cm250.h5\n",
      " Reading Bk245 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk245.h5\n",
      " Reading Bk246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk246.h5\n",
      " Reading Bk247 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk247.h5\n",
      " Reading Bk248 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk248.h5\n",
      " Reading Bk249 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk249.h5\n",
      " Reading Bk250 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Bk250.h5\n",
      " Reading Cf246 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf246.h5\n",
      " Reading Cf247 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf247.h5\n",
      " Reading Cf248 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf248.h5\n",
      " Reading Cf249 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf249.h5\n",
      " Reading Cf250 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf250.h5\n",
      " Reading Cf251 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf251.h5\n",
      " Reading Cf252 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf252.h5\n",
      " Reading Cf253 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf253.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 250K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 294K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at 900K\n",
      " WARNING: Negative value(s) found on probability table for nuclide Cf250 at\n",
      "          1200K\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Reading Cf254 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Cf254.h5\n",
      " Reading Es251 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es251.h5\n",
      " Reading Es252 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es252.h5\n",
      " Reading Es253 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es253.h5\n",
      " Reading Es254 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es254.h5\n",
      " Reading Es254_m1 from\n",
      " /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es254_m1.h5\n",
      " Reading Es255 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Es255.h5\n",
      " Reading Fm255 from /home/lorenzo/Documents/cross_sections/endfb80_hdf5/Fm255.h5\n",
      "Timestep: 0 --> keff: 1.00505\n",
      "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.15837032197505e-16 atom/b-cm)\n",
      "Timestep: 1 --> keff: 0.99648\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.158335355509685e-16 atom/b-cm)\n",
      "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.158332857910594e-16 atom/b-cm)\n",
      "Timestep: 2 --> keff: 0.99569\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.158297094883484e-16 atom/b-cm)\n",
      "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.158233980121496e-16 atom/b-cm)\n",
      "Timestep: 3 --> keff: 0.99108\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.158125377264131e-16 atom/b-cm)\n",
      "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.158023599019991e-16 atom/b-cm)\n",
      "Timestep: 4 --> keff: 0.98848\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.15791131104636e-16 atom/b-cm)\n",
      "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.15776668121522e-16 atom/b-cm)\n",
      "Timestep: 5 --> keff: 0.98764\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.155893001929469e-16 atom/b-cm)\n",
      "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.151049790950367e-16 atom/b-cm)\n",
      "Timestep: 6 --> keff: 0.97911\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.149211007095395e-16 atom/b-cm)\n",
      "[openmc.deplete] t=32400000.0 (final operator evaluation)\n",
      "WARNING: nuclide B11 in material1 is negative (density = -8.145479262213239e-16 atom/b-cm)\n"
     ]
    }
   ],
   "source": [
    "model = openmc.model.Model(geometry,mats,settings)\n",
    "\n",
    "#Depletion general settings\n",
    "depletion_days = [5,5,30,30,30,180,95]\n",
    "power = 8e6 #total thermal power [W]\n",
    "salt_mass = 4590 * 10**3 #grams\n",
    "salt_volume =salt_mass/salt_density #cm3\n",
    "salt.volume = salt_volume\n",
    "\n",
    "# Initialize depletion operator\n",
    "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\", chain_file='ENDF-B-VIII.0_chain_msr.xml')\n",
    "\n",
    "# Initialize integrator object and start depletion calculation \n",
    "integrator = openmc.deplete.CECMIntegrator(op, depletion_days, timestep_units='d', power=power)\n",
    "integrator.integrate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "41d076d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's store some results that will be used later for comparison\n",
    "results = openmc.deplete.Results('depletion_results.h5')\n",
    "t, k_ref = results.get_keff()\n",
    "n_xe_ref = 0\n",
    "n_kr_ref = 0\n",
    "for nuc,_ in openmc.data.isotopes('Xe'):\n",
    "    n_xe_ref += results.get_atoms(str(salt.id), nuc)[1]\n",
    "for nuc,_ in openmc.data.isotopes('Kr'):\n",
    "    n_kr_ref += results.get_atoms(str(salt.id), nuc)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3cd25c",
   "metadata": {},
   "source": [
    "# Removal rates theory\n",
    "The possibility to remove fission products from the fuel salt is one of the key characteristics of the MSRE and msr reactor in general. \n",
    "\n",
    "Mathematically nuclides removal can be thought as an additional term to the Bateman equation:\n",
    "\n",
    "$\\frac{dn_i(t)}{dt} = \\underbrace{\\sum_j \\gamma_{j\\rightarrow i} n_j\\overline{\\sigma_j\\phi} - n_i \\overline{\\sigma_i \\phi}}_\\textbf{R}  + \\underbrace{\\sum_j \\gamma_{j\\rightarrow i} n_j\\lambda_{i\\rightarrow j} + \\lambda_{j\\rightarrow i}n_i}_\\textbf{D} - \\underbrace{\\epsilon_i \\lambda_i n_i}_\\textbf{T}$,\n",
    "\n",
    "Where $ \\epsilon_i \\lambda_i n_i $ is the removal term, $\\epsilon_i$ is the removal efficiency and\n",
    "$\\lambda_i$ is the removal rate coefficient for the continuous removal of the nuclide $i$, expressed in ($s^{-1}$) units.\n",
    "\n",
    "Another way to characterize $\\lambda_i$ is through the concept of cycle time: $T_{cyc,i} = \\frac{1}{\\lambda_i}$, as the time needed to process the removal elements at a specific volumetric rate.\n",
    "\n",
    "For simplicity, we can combine $\\epsilon_i$ and $\\lambda_i$ in one single parameter, that we can call again $\\lambda_i$. Thus, setting a removal rate coefficient of, for example, $1\\ (s^{-1})$ at $100\\ \\%$ efficiency is the same as setting $10\\ (s^{-1})$ at $10\\ \\%$.\n",
    "\n",
    "The capability to model removal rates has been deployed under a different [branch](https://github.com/openmsr/openmc/tree/msr_13.2_cont) of OpenMC.\n",
    "\n",
    "In this example we will set removal rates to gasseous fission products, Xe and Kr, and noble metals fission products. The values and their explanation can be found [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "250cacc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[openmc.deplete] t=0.0 s, dt=432000 s, source=8000000.0\n",
      "Timestep: 0 --> keff: 1.00478\n",
      "WARNING: nuclide B11 in material1 is negative (density = -7.810566471511284e-16 atom/b-cm)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: No intersection found with DAGMC cell 22, material 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[openmc.deplete] t=432000.0 s, dt=432000 s, source=8000000.0\n",
      "Timestep: 1 --> keff: 1.00248\n",
      "WARNING: nuclide B11 in material1 is negative (density = -5.467815330683659e-16 atom/b-cm)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: No intersection found with DAGMC cell 22, material 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[openmc.deplete] t=864000.0 s, dt=2592000 s, source=8000000.0\n",
      "Timestep: 2 --> keff: 1.00147\n",
      "[openmc.deplete] t=3456000.0 s, dt=2592000 s, source=8000000.0\n",
      "Timestep: 3 --> keff: 0.99758\n",
      "[openmc.deplete] t=6048000.0 s, dt=2592000 s, source=8000000.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: No intersection found with DAGMC cell 22, material 1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Timestep: 4 --> keff: 0.99451\n",
      "WARNING: nuclide B11 in material1 is negative (density = -1.0411576378416223e-16 atom/b-cm)\n",
      "[openmc.deplete] t=8640000.0 s, dt=15552000 s, source=8000000.0\n",
      "Timestep: 5 --> keff: 0.99462\n",
      "[openmc.deplete] t=24192000.0 s, dt=8208000 s, source=8000000.0\n",
      "Timestep: 6 --> keff: 0.98593\n",
      "[openmc.deplete] t=32400000.0 (final operator evaluation)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " WARNING: No intersection found with DAGMC cell 22, material 1\n"
     ]
    }
   ],
   "source": [
    "# Re-initialize depletion operator\n",
    "op = openmc.deplete.CoupledOperator(model, normalization_mode = \"energy-deposition\", chain_file='ENDF-B-VIII.0_chain_msr.xml')\n",
    "\n",
    "# Initialize msr continuous object\n",
    "msr = openmc.deplete.msr.MsrContinuous(op, model)\n",
    "\n",
    "# Set removal rates constants in 1/s\n",
    "msr.set_removal_rate('salt', ['Xe','Kr'], 4.067e-5)\n",
    "msr.set_removal_rate('salt', ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'], 8.777e-3)\n",
    "\n",
    "# Initialize integrator object and start depletion calculation \n",
    "integrator = openmc.deplete.CECMIntegrator(op, depletion_days, msr_continuous=msr, timestep_units='d', power=power)\n",
    "integrator.integrate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "415f0334",
   "metadata": {},
   "source": [
    "# Critical Factor\n",
    "As we've set removal rates to fission products, we except keff to drop less rapidly than in the first case, let's verify that: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9877db52",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = openmc.deplete.Results('depletion_results.h5')\n",
    "t, k = results.get_keff()\n",
    "n_xe = 0\n",
    "n_kr = 0\n",
    "for nuc,_ in openmc.data.isotopes('Xe'):\n",
    "    n_xe += results.get_atoms(str(salt.id), nuc)[1]\n",
    "for nuc,_ in openmc.data.isotopes('Kr'):\n",
    "    n_kr += results.get_atoms(str(salt.id), nuc)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "826c26f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W+fpWbq3Ujz2tPdHIqZE60Msb9Llbu2t137pYuuDPgX9W2H0xxhhjFYmDRH1na6t53Lo1BYhHjtC61axI95Lu4asjX+Hao2sIfxSOtJw0reOWMkt1kOhu7Q4TKbXOabX4PQv8fF181a9zt3bHf+P+q8xbYYwxxnSCg0R9Z2QEvPMOYGYGvPIK8P33L/24RKVQIjopGlcfXcXVh1dxLeEarj68ip71euKrTl8BoHx7eVO0yIxkqO9QH42qNUJd+7roVKOT+pi7lTsyp2UWO7aPMcbYixECyM4GsrJoUz12cQHMaSElxMUB165pH8/KApycgO7ddVv/lxEHiYZgA60wgawsChYfPgTCwwFvb93Wq4IJIZCWk6ZOE/Mk4wm6/dZNvXpHfi6WLlqP53aZi3oO9dDIqRFq29eGsbTwX3eJRAIJXs6AmzFWtSUnA4mJhQdnWVlAhw6AxbPh0efPA+fOaR/PWz4oCPD0pLLbtwPr1hUso/r622+adSB+/BGYPBnIySm8jnv3At260eM9ewpfWOzVVzlI1AUOEg2JXA60bw8cOkRdzlUkSBRCICY5Rt0yePXRVfX4wW51uuGPAX8AAGxNbXH10VWk56TDRGqC+o714e3kjUZOjdDIqRF8XDSrekgkEnz+yue6uiXGWBUnBJCbWzBAqpFn1cbr12kNhPwBlOrx6NGAybMFf/78Ezh9uugA7bffABsbKjt3LrBxY+HnzMqiNRe8vKjsrFnADz8UfR///Qc0akSPd+0CZs4suuzbb2uCxIgI4O+/iy6blKR5LJUWHiAaG9PbmjJPylcnJ6BxY9ovk9FXuRxo0qToa7GKw0GiIRCCPg4aG9M6zqog8cMPdV2zUlEFg4mZiWjiTH/xSqGEyw8ueJT+qNDX3Ei4oX4slUjx99t/w93KHXXs6xS6nBpj7OUhBAVFKSn01cNDc+zQISAmBkhNBTIztQM1Y2Pgm280ZWfNAi5eLDw4A4CwME3Zt98Gduyg46rMZHnl5ND5AQq4Nm8uuv5DhmgCv+Bg4Ndfiy6bmqopGx8PXL1adFlVvQFKsasKtvIGXarnRnmyXnl7A336FF5OLqduYZWuXQFr64JlVF+barJ2YcgQ4I03CpaVFjLCp1cv2ph+4CDRELz9NrB1K7BkCc1wBoCjR+njV2F/ZXoiLTsNW65uwanoU+qWweSsZDRzaYYL718AQIGfi6ULnmY+RV37umhUrZG6ZVA1fjAv/1r+urgVxlg5UCgoYLPQZH/Cv/8Cjx5REJSSov3VwgL4+mtN2SFDKGDLW06hoGOurtRqpzJ9OnDqVOH1sLTUDhJPngT27Su63nmHgKtaD/OTSinwUQWhAFC9OgVeRQVdeYeVBwTQPMWigi5VgAgAY8dSIJU/8FM9rqbJp49Zs2griYEDaSuJpk21A8HiWFvTxgwPB4mGQDXD+elToFUrGuGbkEAfJfW0Df6rw19h+dnleJr5VGu/sdQYUokUQghInv2H3Dt0L5wsnCAzkumiqoyxErh6lboQ8wdyKSn0L2rsWE3Z4cOBO3c0ZVTlMjKAhg1pYoLKu+9qP8/L3V07SLx7l4ZjF0YVLKq0bUurmVpaUmta3kAqb5AKUKdMnz5FB115LVsGLFhQsJxxIe+m//sfbSUxYABtJVG/Pm2MVTQOEg2BKqH206f03+iVV4D9+2mWs54EiXmDPgCISorC08ynqGVXC4MbD0aTak3QqFoj1HOoVyAYdLd2r+zqMlZlxcZSQJY/mEtNBRwcgLfe0pQdM4a6LgtrxWvaFDh2TFM2IIDOXRhvb+0g8dy5ooO51Hxzzpo2paDN0lIT1Km+Ojlpl12wgFoi85e1sNDuNgWA+SVbaAgA0LNnycvm7XJlrCyio6MxbNgwPHz4EMbGxvjqq68wYMAAJCYmwt/fH7m5ucjNzcWkSZMwZswYndaVg0RDkDdIBKjLef9+Gpc4YYLOqgUAmbmZ2HRlE5acWYL1fdarxxp+/srnGOA9AD3q9ijRWsCMvUyEoGCnsFa51FTA0VEzskQImhlaWOCXkgK0aQP8nmexngYNCgZiKu3baweJ//xDKUcKk5io/bxmTWqRKyyYq15du+z331OXbP5yqq95bdr0nG9WHm2LznnPmMEwNjbGokWL4OvriwcPHqBFixbo0aMHrKyscOzYMZibmyMtLQ2NGzdGv3794ODgoLu66uzKrORUq648eUJfX3uNvu7bRx/t3Su/Je5+yn38ePZHrDy/EgnpCQCA5WeXY8WbKwAA3k7e8HaqGrOvGcvN1e42VX11cAB8nk2qz8qiWaeFBX0pKUCnTppZpgqFJi9cYXr00ASJEgnwyy9AenrhZd3yrdBoZ0ddn4UFaPkTIsyeTfemKpO3fN48/gBw/HhJvlPkzTdLXpaxl42rqytcXV0BAC4uLnB0dMSTJ0/g6ekJ82f/GLKysiCEgChsdlQl4iDREORvSWzdGmjXDggNBaZOBdavL/q15ezfmH+x5PQS/HHtD+QqcwHQUnXjW4/H6OaFJLdirJIJQWPf8o+ZU6UESU0F1qwpuhXP3x/45BMq+/QpBWGZmYVfa/BgTSueVFp8+hBnZ81jY2NqlVNN4sgbnFlaalKSqEybRucvrFXO0VG77L17KLHAwJKXZYyRY8eOYd68eTh//jzi4uKwfft29OnTR6vM8uXLMW/ePDx48AA+Pj5YunQpWrduXeBc58+fh0KhgOez3EKJiYno1KkTbt26hXnz5sEx/x94JeMg0RDkDxIlEmDxYgoWN2ygUddt2lR4NbIV2eizuQ/i0+IBAB2rd8TENhPRp0GfIhNVM/Y8ubmaAM3cnFrnAJoksXt30cFcQAAwYgSVjY6mrkhVa1/evGsAjZf78Ud6nJEBTJxYdH3y/k+2sNAOEFUtdKoALW8rnokJMH485bsvrBUvf4P/w4d0v/nH0hVm6tTnl2GMVY60tDT4+Phg1KhR6NevX4HjW7ZsQVBQEFasWIE2bdpg0aJFCAgIwI0bN1Atz9TzJ0+eYPjw4Vi1apV6n62tLS5duoT4+Hj069cP/fv3h3PeT5iVjN/ZDUH16kD//kCtWpp9rVrRO+S6dTRg6dSpcl+mLz41HhuvbMSkNpNgJDWCzEiGSW0m4cbjG5jUZhKauTYr1+sx/ScEdXtKpRQMARTMnTpVdDdr9+6UIw0Abt6kMXF5u27zphOZOhWYM4ceJyTQipRFsbPTBIlyuXb6ExULCwrQ8nbtWlsDgwYV3ipnZQXUzZN1SSYDIiM1x/PPdM1v6dLij+dlZVXysoyxipWSkoLk5GT1c7lcDnkRf/Ddu3dH92KWf1mwYAHGjBmDwGdN9StWrMA///yD1atX4/PPaZGHrKws9OnTB59//jnat29f4BzOzs7w8fHB8ePH0b9//xe5tRfCQaIhqFsX+OOPgvvnzKE0/f/+S31eQ4eWy+UuxF3A4tOLsfm/zchWZKOufV30rE/T/6Z25CYNQ5I3v1tyMs04zT/jVfW4a1daogsALl+m1rbCZsgKAXz3HfDZZ1T29m0aQ1cUGxtNkCiR0AoPhZHJtNOY2NpS129Rkx+a5fmM4uBAS4rl7bK1sCg8jahcXnyC4/xU3dSMsarLO9+A3RkzZuDrvPmXSig7Oxvnz5/H1DzN/1KpFP7+/ggNDQVA2UBGjhyJ1157DcOGDVOXi4+Ph7m5OaysrJCUlIRjx45hbN60ATrAQaIhc3MDvvgC+PJLesfu06dgArASylXmYnv4diw5swQn7p1Q72/j3gYWsrKdk5VNejq1ihXVzdqlCy1bBVBgNHt20a14S5YA779PZc+do9cWxcJCEyRmZVG+9qKkpGge29sDzZsXDOJUjzt10pT18AAOHCg86JPlS5Pp4EBlS8LIiOrAGGNlce3aNbjnGRNSVCvi8yQkJEChUBToInZ2dsb169cBACdPnsSWLVvQtGlT7NixAwCwYcMGZGRk4L333lNPWJkwYQKa6DjNHQeJhkK1NJ+FhXbW1qAgYNUqyjL7v/9pLyNQQg9SH6D1qtaITo4GQAmvBzYaiImtJ6KNR8WPdXwZxMVRg+/9+5otLo6+JiRQN6UqNUlwsHaakvxWrtQEiU+eANu3F102bzBnZ0etYkV1s/r6asrWrUutbUW14uXtvq1Zk4LVkjAzo9ZBxhjTJ1ZWVrCupGVhXnnlFSjzD5x+JizvGpB6gINEQ+HqSllv867GDtAUyR9+oDGL8+bR8gV5V5gvwsO0h6hmQQNonS2c4WThhMzcTLzf4n2MbTUWblZuzzkDS0mhMXb5gz7VNmcO0K0blT1+nMbBFUWV3QjQLGFVWKucpSUFZSqNGtGEjKKCubyTMJo1o/F1JWFrW3x9GWOMFeTo6AgjIyPEx8dr7Y+Pj4eLAWZi5yDRUFhZUZD49GnBY/36UZ/e0aPAlCnAli2FnkKhVOCfW/9g8enFOBN7BjEfxcDG1AYSiQRb+2+Fu7U7TI1NK/hG9JsQwOPHFOTFxmoHfffv0xyhzp2pbHBw8eucRkRoHteqRRPQ3dwKbk5O2uPe/P1pMkhJuLlpr3TBGGNMd2QyGVq0aIFDhw6p0+IolUocOnQI48eP123lyoCDREORP6F2XhIJsGgRDcraupXycHTsqD6clJmE1RdXY9nZZbjz9A4AQCqR4ljUMfWElNr2tSv6DnQuN5da0u7e1W79GzJEs5LDtm3Fr5/6+uuaINHDgxp48wZ87u6ax6okywDQsiV1NzPGGDNsqampuH37tvp5ZGQkwsLCYG9vj+rVqyMoKAgjRoxAy5Yt0bp1ayxatAhpaWnq2c6GhINEQ5E/V2J+vr60EOvPP1PT0qlTuC9JxZzjc7A2bC3SctLoNKZ2GNN8DD5s/SGq21Qv/FwGLj2d8uSplv86epRSSd66BWRnFyxft64mSFTlvatWTTv4UwWDr76qeV27doWnXWGMMVZ1nTt3Dp1VrQUAgoKCAAAjRozA2rVrMWjQIDx69AjTp0/HgwcP4Ovri+DgYJ3mOywrDhINxfOCRACYNQvYsQO4ehXo1w+5m1ZgxbkVUAgFvJ28ManNJAxtMrTKzFZ+/JhSuly/Tl9VW1QUTQT58EMqZ2ZG3xLV41q1tFv8WrbUnLN1a5rZm3+mLWOMMQYAfn5+z10ub/z48QbZvZwfB4mGorju5mcSLKWYN78b/vfBNuDQIVSfNB1zx81BM7fm6FKzCyTlnGy7MghBq2mEh1NAp8oGEBoKFJJ/VC3vBI3GjYE9e4CGDSkveWG581SM+S+CMcYYA8BBouF4TkuiUigxbPswBEcEY+Rvq9BwwFhg0yZ86uoKzDeMnCNpacC+fdqtg9ev034A+OgjYMECelyvHn319KTgL/+Wd1avuTmt+sEYY4yxkuMg0VD4+lKam7zJ7PL434n/Ifh2MEyNTZHTrjWwZg0wbBhFVW5uwMcfV2p1i5Kaqt097O2tWXotJaXw/IDGxjRuUNWYClCi5ZQUzbhDxhhjjJUvDhINRf/+tBXiWNQxTDsyDQCwrPsyNHVuCrzTlKbuTpkCfPIJzbwYMqQyawyAJpFMnaoJCmNitI/36qUJEp2dAT8/mjWct1Wwdm3AxKTguTlAZIwxxioOB4kG7mHaQwzeNpi6m5sOw6hmozQHP/mEpt8uWgSMHElTdithuYukJFqvF6Bc37/8QsGiSrVqmgBQtQwcQJl8jhyp8OoxxhhjrAQ4SDQkSiVFW8+a0FTjEO+n3EdDx4b46Y2ftCenSCTA/PnUorhlC9C3L3DsGC29Uc7S0ugSP/9MQeK1a3R5qZQmXVtbawLDvN3GjDHGGNNPHCQaimvXaGqvgwPw8CEA4J+b/2B/xH6YGZth64Cthae2kUqBdevoNUeO0AyOU6coD0w5uHCBlo7euFGzTrCxMY07bNiQnj9LIcUYY4wxA8JBoqGwtqaWxKdPKS+MRIKe9XtiXZ91kEqkaFytcdGvlcuB7dspE/Tly7Sg8MmTtB5cGR06BHz2GXD+vGZfnTrA6NHUs22AOUMZY4wxlgcHiYZC1Uebm0tThK2sAADDfYaX7PU2NsDevZRc8NYt4I03gMOHSzz7QwharUQu1+w7f56STvfrR4u9+PkVn4OQMcYYY4aD39INhZmZZhmQ4lZdKY6bGyUidHAAzp4FBg4EcnKKfUliIrBsGa1D/MUXmv2dOwM//QTExgKbNgGvvcYBImOMMVaV8Nu6oZBI1K2JZyOOw3quNZqtLMMElPr1gd27Kejcuxd47z1qJsxDCODECWDECMqcM2ECcOUKsHUr9XgDFBB+8IF20mrGGGOMVR36FSQeOwb07EktXhIJrUP8PCEhQPPm1A9apw6wdm3BMsuXA15elI+lTRvgzBnt45mZtNCvgwN1v771FhAf/+L3U96erbqSmZSAlOwUpGWnle08bdtSxGdkRN+vadPUh379FWjUCOjYEVi/nr41TZoAS5bQcEZuLWSMMcZeDvr1lp+WRv2ay5eXrHxkJI2t69wZCAsDJk+mmRP79mnKbNlC02tnzKCpuD4+QECAeoYwAFrvbdcu4I8/gKNHKbdgv37leWfl41mQmJOcCAAwMSokw3RJvfkmsGIFBAAxZw71KQO4epWSXpubA6NG0RrJly5Ra6JqZUDGGGOMVX36NXGle/fSLbK7YgVQsyblAgQo58qJE8DChRQIArQs3ZgxQGCg5jX//AOsXg18/jkl9fv1V+D332lgHUBL2jVsCPz7L7W65ZeVRZuKKvdLRevcGXBzQ44NTTaRGcnKfKqHD4F1T0fjF4e++PHxQHSZOBFwccHYsf1Rrx4tzmJtXV4VZ4wxxpih0a+WxNIKDS24gkhAAO0HaDru+fPaZaRSeq4qc/48Td7IW6ZBA6B6dU2Z/ObOpdnCqs3bu/zuqTjffgv88QeyG9QDAJhIS9eSqFQCBw7QfBUPD1qx7+ZjB6yrN5sGIr7zDurGHcMHH3CAyBhjjL3sDDtIfPCgYEI+Z2cgORnIyAASEgCFovAyDx5oziGTAba2RZfJb+pUaoFUbdeulcvtlFSOkmYkl7QlMTsbmDOHhmx27Uq96jk5QOvWlAj7xzOtgD59qHW0Vy+apcIYY4yxl5p+dTcbCrlcO2FgcnLlXVupRE4mTVgp6ZhEExOahBIZSS2Ew4ZRD7yPj6qEEXW3v/46JdlWrcpSvXrF3ANjjDHG9J5htyS6uBSchRwfT5GQmRnlZzEyKryMi4vmHNnZlBCwqDL6YuVKwMQE9kt/QQfPDmjsVHCVldhY4JtvKADMyKB9EgntW7uWlnFW5T3UYmYG7NxJXeexsbQqy5MnFX5LjDHGGNNPhh0ktmtH68PldeAA7QeoG7lFC+0ySiU9V5Vp0YKa2vKWuXEDuHdPU0ZfmJsDSiVej5HjxKgTWNpjqdbhM2coDeKMGZSuZts2zbGBAynvobl5Mee3tweCgwF3d5ri3KuXJtJkjDHG2EtFv4LE1FRKZRMWRs8jI+nxvXv0fOpUYHieZeg++AC4c4dmYFy/Dvz4I+X/++gjTZmgIBp4t24dBT5jx1KqHdVsZxsb4N13qdyRIzSRJTCQAsTCZjbrkioHTSEtfDExQO/edGvNmwMbNwL9+5fhGp6eFCja2FDX8+DBtBQgY4wxxl4q+jUm8dw5SvOiEhREX0eM0PSVqgJGgNLf/PMPBYWLF9OU3V9+0aS/AYBBg4BHj4Dp02kiiq8vBUF5J7MsXEiznt96iyZvBARQwKlvVOs351uWLy2NGv0ePAAaN6ZY94VmJzduTF3PXbsCf/8NjB9Pa/BJJC9wUsYYY4wZEokQ+dZkY6UWExMDT09PREdHw8PDo+IuFB4OeHvjp1fN8U0vawz0HoiFAYsxYADw11+AkxN1OXt5ldP1tm0DBgyg9DjffAN89VU5nZgxxhjTvUp7/zZQ+tXdzIr3rCUxSZmOB6kPkJKdgoQEWiVFJgO2by/HABGgltVnK7Fg+nRqpWWMMcbYS4GDREOiWpbv2U/NRGqCatVoYZhdu4AOHSrgmuPGAV98QY/ff58uxBhjjLEqj4NEQyKTAT16IKdxQyDbXJ1M29aWhg9WmG+/BUaOpJnhgwYVvRINY4wxxqoMDhINzT//IK37W8DSG/hvd2dUyohSiQT4+WdKsp2RAbz5Js0mZ4wxxliVxUGiAXoYZQekeOD4L72QnV1JFzUxofX8WrWiFDzdugH371fSxRljjDFW2ThINECZWQoAgKVDitbqgBXOwoJSDtWtC0RFUctiUlIlVoAxxhhjlYWDREMzbBjs1tHkEblMB3kLnZyAffsoz+Tly0CfPpRbkjHGGGNVCgeJhkYmw6DLJgCAata2uqlDzZrA3r2AlRUQEkKr4CiVuqkLY4wxxioEB4mGxs4OOaAg0cREh/Vo1owyeJuYaJZC5LzsjDHGWJXBQaKhsbNDNij1jUym47r4+9Oa2ACwZAkwb55u68MYY4yxcsNBoqGxs8PyFkaANAeJOY90XRtg8GBg/nx6/NlnwIYNuq0PY4wxxsoFB4mGxt4esno7gOkyfPLzTl3XhgQF0QYAo0bRxBbGGGOMGTQOEg2NnR2yjeih3FjX/c15zJtHrYq5ubTm87lzuq4RY4wxpneio6Ph5+cHb29vNG3aFH/88Yf6WN++fWFnZ4f+/fvrsIYaHCQaGg8P5DjSGs4mRrqcuZKPVAqsXUvjFNPSgB49gNu3dV0rxhhjTK8YGxtj0aJFuHbtGvbv34/JkycjLS0NADBp0iSsX79exzXU4CDR0DRqhAfKd4HNfyF4vbeua6NNJgO2bQN8fYFHj2hVlocPdV0rxhhjTG+4urrC19cXAODi4gJHR0c8efIEAODn5wcrKysd1k4bB4kGKC3eDbjeF5HX7HVdlYKsrSmHYs2aQEQEtSimpuq6Vowxxli5OHbsGHr27Ak3NzdIJBLs2LGjQJnly5fDy8sLpqamaNOmDc6cOVPouc6fPw+FQgFPT88KrnXZcJBogHJz6KvOU+AUxcUFCA4GHB2B8+dpVRYOFBljjOmplJQUJCcnq7esYlYSS0tLg4+PD5YvX17o8S1btiAoKAgzZszAhQsX4OPjg4CAADzM17P25MkTDB8+HD///HO53kt54iDRAFnfpfWSzXIydVyTYtSrB+zeDZibA4cOAZ06AQ8e6LpWjDHGWAHe3t6wsbFRb3Pnzi2ybPfu3fHtt9+ib9++hR5fsGABxowZg8DAQHh7e2PFihUwNzfH6tWr1WWysrLQp08ffP7552jfvn2530954SDRAA2MoIkrnjJLHdfkOdq0AQ4fphbFCxeAdu2A69d1XSvGGGNMy7Vr15CUlKTepk6dWqbzZGdn4/z58/D391fvk0ql8Pf3R2hoKABACIGRI0fitddew7Bhw8ql/hWFg0QDlGNKg1pligwd16QE2rQBQkOBOnWAu3eB9u2BEyd0XSvGGGNMzcrKCtbW1upNLpeX6TwJCQlQKBRwdnbW2u/s7IwHz3rTTp48iS1btmDHjh3w9fWFr68vrly5AgDw9/fHgAEDsGfPHnh4eKgDS10x1unVWZlky6kFUaZI13FNSqhOHeDUKaBnT+D0aUqT89tvgJ7kgWKMMcYqyyuvvAKlUlnosYMHD1ZybYrHLYkG6HfHRABASlaCbitSGk5O1PXcuzeQlQUMHAgsXKjrWjHGGGPlxtHREUZGRoiPj9faHx8fDxcXFx3Vquw4SDRA6W9MBKbJ8UGb7bquSumYm1MexXHjACFoKb+PPgKK+ETFGGOMGRKZTIYWLVrg0KFD6n1KpRKHDh1Cu3btdFizsuHuZgOUK1UCkhw8TI3GjYQbWscExHNfL0QJylTkeaZ/AHjKqSXx90XAo6vA3LmAXF6ic5a2ngVeU8HXqIx7MASl/T4YCv55GZbifl7F3fPzfs5lfW1FvE4X19TF98dabo02Hm2KvW5lSE1Nxe08K4pFRkYiLCwM9vb2qF69OoKCgjBixAi0bNkSrVu3xqJFi5CWlobAwEAd1rpsOEg0QFkSBQDgdcu/gOV/6bg2L2Cc6sEBYPUBXdaEMcaYnmvh2gLn3jun62rg3Llz6Ny5s/p5UFAQAGDEiBFYu3YtBg0ahEePHmH69Ol48OABfH19ERwcXGAyiyHgINHACCGQGzoBuN8S8F0Hu0bnC5SRSCSlOqcEpSxfnufPyQGSkqj72cgIsLWFxMio/M5f1GsM6XukZ0p7b7rE39eKU5W+t8Xdy4u89nmvf5HX6vO1K6redR3qFvu6yuLn5/fclt3x48dj/PjxlVSjisNBooFRCAUQ9SpwvR8cGlxDwmdVoAXu6lWge3cgOhpwNgb++Qdo0ULXtWKMMcZeajxxxcBkK7IBBa3HZ2yi48qUl0aNKJdi06ZAfDytzrJnj65rxRhjjL3UOEg0MAqlAibCAgBgEn8PuHlTxzUqJ+7uwPHjlEMxLQ3o1Qv45Rdd14oxxhh7aXGQaGCs5FZoYN8EACATOcCgQUCmHq/hXBrW1tTVPHw4oFAAY8YA06fTeEXGGGOMVSoOEg1Qdg4N6pUZKYCwMODTT3VbofIkkwFr1wJffUXPZ80CAgNpggtjjDHGKg0HiQYoJ5u+mlR3pQfLlgHbDSyxdnEkEuCbb4Cff6YZz+vWAW+8ASQn67pmjDHG2EuDg0QDcy/pHqKfPgQAyBysNK2Io0YBUVE6rFkFGDMG2LmTVmo5cAB49VXg/n1d14oxxhh7KXCQaGBSs1ORM7IlMMUerk1uALNnA23aAImJwPvv67p65a9HD+DoUaBaNeDSJaBtW0qZwxhjjLEKxUGigclR5ACydMD8KUzlUsDEBNi8mWYFL1+u6+pVjJYtgX//BerXp1yKHToAISG6rhVjjDFWpXGQaGCyFdnqxzIjypcILy/qjq1dWzeVqgw1awInT1KAmJQEBARQcMwYY4yxCsFBooHJUeYAwQuAXSuQk1it8EIHDgCxsZVbscrg4ED39tZbQHY2MHgwMG8ep8hhjDHGKgAvy2dgshXZwKVhQIYj4s7+jl1OtF+91OW+fZAsWwp4ewNz59LsYOQrk4+u9pfkeMFjZsB7WwCxEpK//gSm7AVOyoEPPyzRvZbtmhV7rKjj5fF91pdzVOS59eUcFXnuqnSOijx3Wf8HPU95LKvNdXjxcxgZAZaWL14HVnIS8bxVqtlzxcTEwNPTE9HR0fDw8KjQax2IOICuDdsCOVYVeh3GGGNMn7RoAZw7V77nrMz3b0PELYkGRiKRAEpatFlqnIuWzbV/hEIAePIEiLhNO+rWA2xtC+2R1dW+ci2TlATERANCQJiZAzVqAEbF/1oXd73KPlbU8aJeU5r9+nKOijy3vpyjIs9dlc5Rkefm5g7Gyp/+BYnLl9M4swcPAB8fYOlSoHXrwsvm5FCX6rp1NAavfn3gf/8DunXTlElJodU7tm8HHj4EmjUDFi8GWrXSlBk5ks6RV0AAEBxc7rf3ovxr+QNKJQDAyjEZp0/bF1LKHpj4G33vHtsDBy8C1atXbkUrjQ1w/DLQuzfw9CmQWwfYHVy1J/EwxirciwSdZX2tIV1TF3Utj+5uVjr6FSRu2QIEBQErVlDuv0WLKFi7cYPy5OU3bRrw22/AqlVAgwbAvn1A377AqVMUDALA6NHAf/8BGzYAbm5U3t8fuHYNcHfXnKtbN2DNGs1zubxCb7WsFAoAguYbGZsoiy44bx6ljTl7ltZ3PnqUlryrijp2pJnP3bsDt28D7doBu3cX/eGCMcae40UCEg5mWFWhX7ObFyygVTYCA2nixYoVtNrG6tWFl9+wAfjiC0q4XKsWMHYsPZ4/n45nZADbtgHff0+rddSpA3z9NX396Sftc8nlgIuLZrOzq9BbLatsTQYcmMgURReUyynotrWlYHHbtgqvm041bAiEhgLNmwOPHgF+frRaC2OMMcbKRH+CxOxs4Px5auVTkUrpeWho4a/JygJMTbX3mZkBJ07Q49xcanorroxKSAi1VtavT8Hm48dF1zUri9YRVm0pKSW6xfLwz/X96scmsmJaEgHKLbh+PQXZgwdXcM30gKsr/Ry7daMPCH37FvwwwBhjjLES0Z8gMSGBAjpnZ+39zs40PrEwAQHU+njrFo3TO3AA+OsvIC6OjltZUdfjrFm05q9CQd3NoaGaMgAFFevXA4cO0ZjGo0ep61JRREvd3LmAjY1m8/Z+8fsvoYTcu0DP0QAAmfw5QSIA9OxJLbMvCysrakF89136nRg3Dpg6VT2OkzHGGGMloz9BYlksXgzUrUvjEWUyYPx4CoikeW5rwwYaJevuTl2wS5ZQq1reMm+/DfTqBTRpAvTpQ+PZzp4teum3qVNpVq1qu3atIu9Si0LkACYZAAATWSlH/z5+TGM+MzMroGZ6xMSExqnOnEnPv/sOGD5cu6+eMcYYY8XSnyDR0ZEyZcbHa++Pj6cxgoVxcgJ27ADS0oCoKOD6dcq0WauWpkzt2tQymJpK6/6eOUOzovOWya9WLarP7duFH5fLAWtrzWZVeTkLsxXZgEL2rBqlCBKFAF5/HVi4EPj44wqqnR6RSIDp06mr3dgY2LiRWowTE3VTHyEoSE1Opln29+4BN28Cly9rt2ozxhhjekJ/ZjfLZJQp89Ahas0DqIvw0CFqISyOqSm1FObk0ASNgQMLlrGwoO3pU5oF/f33RZ8vJoZa3Vxdy3w7FeVJgglwaTgAwKQ0k5UlEmDOHOpG//FHmsgzaFDFVFKfBAbSrPb+/YEjR2gm9ObN9LuQmVm6LSur9K/J+9qi8j5IJMAbb1DXeECAdis3Y4wxpiP6EyQC1BU6YgTQsiWlL1m0iFoJVWPqhg+nYHDuXHp++jTlR/T1pa9ff02B5ZQpmnPu20dvzvXrU8vgp59S97TqnKmp1C351lvUYhkRQa+vU4fesPVM0hMT4G5nAIC8tN3N3brRbPA5cyg1ULNmQL16FVBLPRMQABw7RoHYf/8BjRvrukbUGm1qSl8fPqQhDrt3a2bpBwbSWtWMMcaYjuhXkDhoEKUvmT6dJqv4+lJCa9Vklnv3tFtZMjMpV+KdO9TN3KMHjUG0tdWUSUqiMYQxMYC9PQWDs2fTuDWAurgvX6Zk2omJ1OrUtStNdtHDXImZWZoJGDJZGZJxzZxJOQWPHgUGDKD0OGZm5VhDPdWsGU1YGjSIhhyYmVGQln9TBW+l2Ur7GplMO5HajRuU7mnNGvpd/vRTSgD/9tvUupg38TtjjDFWSXjt5nJQmWs/jvlpFX4ZNwYA0H1QNPZs9iz9SeLiKAB/+JBaFFetKt9K6jsh9DPbbVoasGkTrToUFqbZ37Il8OGHFOC+DAE9Y4xVEl67uXg8+MnAjGgyRv24TC2JAI21/P13CpQOHaK1nl8m+hggAjROcvRo4MIFWjXonXeo1fHcOep+dncHPvmk6AlVjDHGWDnSr+5m9lx5s7iYyl8g2OnShVZkef117e55pnsSCeX3bNeO8oD++it1R0dF0WpC8+fT+NJx42iIhZGRrmvMGGOski1ZsqTUrwkMDIRVKTKycJBoYHJyNI9N5S/YEDxgwIu9nlU8Jyfg889pnOLevdQVHRys2WrUAD74gJKHOznpuraMMcYqyeTJk+Hh4QGjEjYUREdH48033+QgsSr74/LfAHoDAMxMy2m0gBDAypXUzblypf52x77MjIyAN9+kLSKCWhZXr6bWxalTgRkzKOj/8EOgbVv+GTLG2Evg3LlzqFatWonKliY4VOExiQYmo/puwHsLgHJoSVQJD6fgYtUqCjyYfqtdG5g3j2bsr1lDE1uysylhePv2QPPmwC+/0EQYxhhjVdKMGTNgaWlZ4vJffPEF7O3tS3UNDhINjNI4FTDOAlCOLYne3pQWCKDE5Zcvl895WcUyMwNGjqQlJM+cocempjQzeswYmugyeTKt7MIYY6xKmTFjBszNzUtcfurUqbAt5RwEDhINTI4iB1BQ/sZyCxIBSiDeowflnuzfn5aPY4ajVStqVYyJoVbGWrUoR+jixZRI/vXXge3bgdxcXdeUMcZYOcvIyEB6err6eVRUFBYtWoT9+/e/0Hk5SDQw8Te8gLhmAABz03IcUiqVAuvXA56ewK1bwHvvFb2MHNNfDg6UJufWLZro8uabND7x4EGgXz+gZk3g228pWT1jjLEqoXfv3li/fj0AIDExEW3atMH8+fPRu3dv/PTTT2U+LweJBuZxRA3gCS2lZ2ZazqlPHBwoLY6xMX1dsaJ8z88qj1RKaXJ27aJVXD7/HHB0pJbGr74CqlcHBg8Gjh/nDwOMMVbJ+vbtCzs7O/Tv319r/w8//IBGjRqhcePG+O2330p8vgsXLqBjx44AgD///BPOzs6IiorC+vXry5QqR4WDRAOTnaN5Q7coz5ZElXbtgP/9j4KM1NTyPz+rfF5etN55dDQtW9muHeVS2rwZePVVwMeHPhCkpOi6powx9lKYNGmSuuVP5cqVK/j9999x/vx5nD17FsuWLUNiYmKJzpeenq6evbx//37069cPUqkUbdu2RVRUVJnryUGigcnN0fzIzMu7JVHlo48oHc6nn1bM+ZlumJrSKi6nTgHnz9PqLmZmwJUrwNixNNFlwgTg2jVd15Qxxqo0Pz+/AilpwsPD0a5dO5iamsLMzAw+Pj4IDg4u0fnq1KmDHTt2IDo6Gvv27UPXrl0BAA8fPoS1tXWZ68lBooEZ3HC4+rGZWQUFiRIJtS6pZGZyl2RV07w5pTyKjQUWLgTq1qWWxGXLgEaNgM6dgT/+0M7ezhhjDMeOHUPPnj3h5uYGiUSCHTt2FCizfPlyeHl5wdTUFG3atMGZM2eee97GjRsjJCQEiYmJePr0KUJCQhAbG1uiOk2fPh2ffPIJvLy80KZNG7Rr1w4AtSo2a9asVPeXFweJBiYzW6F+LJNVwgVv3KCZs4sXV8LFWKWzs6M0OdevAwcOAH360FCDkBBg4EBa0eXrr4H793VbT8YYq0ApKSlITk5Wb1lZWUWWTUtLg4+PD5YvX17o8S1btiAoKAgzZszAhQsX4OPjg4CAADx8+LDYOnh7e2PixIl47bXX0K9fP7Rt27bEq6n0798f9+7dw7lz57RaH7t06YKFCxeW6ByF4SDRwGRmKdWP5fJKuODhw8B//1HX87//VsIFmU5IpYC/P6XJuXsXmDYNcHYG4uKAmTNposuAAcCRI9yqzBircry9vWFjY6Pe5s6dW2TZ7t2749tvv0Xfvn0LPb5gwQKMGTMGgYGB8Pb2xooVK2Bubo7VJVis4v3338eFCxdw5MgRmJiYoG7duiW+BxcXFzRr1gxSqSa0a926NRo0aFDic+THQaKBORoRqn5cKS2JH3xAwUFuLjBoEPDkSSVclOmUpycwaxZw7x6waRPwyiuAQgH8+Sfw2mvUHb1sGeVhZIyxKuDatWtISkpSb1OnTi3TebKzs3H+/Hn4+/ur90mlUvj7+yM0NLSYVxJVa+ONGzdw5swZBAQElOi6mZmZmDdvHnr06IGWLVuiefPmWltZ8drNBiax2XTgUEsgzaVyWhIlElri7eJF4PZtYMQI4O+/qeWJVW0yGfD227Rdvgz89BPNjg4Ppwkun39OE2E+/BBo0kTXtWWMsTKzsrJ6oQkeKgkJCVAoFHB2dtba7+zsjOvXr6uf+/v749KlS0hLS4OHhwf++OMPtGvXDr1790ZSUhIsLCywZs0aGBuXLEx79913sX//fvTv3x+tW7eGRCJ54XsBOEg0OAqLWEBQgFYpLYkAYG1NrUht2gC7dwM//EArtLCXR9OmFCR+9x0Fij/+SMHiypW0vfIKBYv9+lXiLyZjjBmmgwcPFrq/JK2Nhdm9ezf27NmDDh06vEi1CuDmIAOTK3IBBb0JV0pLooqPD7B0KT3+4gvgxIlKvDjTGzY2tL731as0XrV/f8DIiH4fBg+mrupp0ygnI2OMvWQcHR1hZGSE+Ph4rf3x8fFwcXGpsOu6u7sXSKlTHjhINDDpl7oBORYAdNBgM3o0dS+2bUsJmtnLSyLRpMmJigJmzABcXYGHD4HZs+n3o29fmjGtVD73dIwxVhXIZDK0aNEChw4dUu9TKpU4dOiQOi1NRZg/fz4+++yzF0qcXRgOEg1MzpXegNIEQCW3JAIUGKxcSTNcPTwq+eJMb7m7U5qcqCgKGjt3psBwxw6ga1egYUNg0SLg6VMdV5Qxxl5camoqwsLCEBYWBgCIjIxEWFgY7t27BwAICgrCqlWrsG7dOoSHh2Ps2LFIS0tDYGBghdWpZcuWyMzMRK1atWBlZQV7e3utrax4TKKBUeRqmg91MvTL3Fz7+a1blIiZMRMT6n7u359WbfnpJ2DdOuDmTVrF54svgKFDgXHjgBdI7soYY7p07tw5dO7cWf08KCgIADBixAisXbsWgwYNwqNHjzB9+nQ8ePAAvr6+CA4OLjCZpTwNHjwYsbGxmDNnDpydnctt4opECE569qJiYmLg6emJ6OhoeFRwC5uk7h7gdg8AtLSyhUWFXq5oSiXlTly0iLoUX3tNRxVhei0lBdi4EVi+nPJtqrRtSxNd+ven5QIZY0wHKvP9uyKZm5sjNDQUPnlXSysH3N1sYOrban4BdDqJVCoFEhMpWBwyBHjwQIeVYXrLyopybV6+DBw7Rul0jI0pMfuwYTTRZepUSuDNGGOsTBo0aICMjIxyPy8HiQYmN0ezRE8J0ydVnKVLKT9efDzNbFUonv8a9nKSSICOHSk5d3Q0Jev28AASEiitTq1aQM+ewN69PNGFMcZK6bvvvsPHH3+MkJAQPH78WGuJweTk5DKfl7uby0FlNlfX8I7HvXBnSIxyoczVdZQIWtu5ZUvq+542jd78GSuJ3FzKu7l8OZA3Z1itWsDYsUBgIODgoLv6McaqvKrS3axaii//WEQhBCQSCRRlbMThILEcVNYvWUpWCqrVfoDM2LowkmUhN6uypzcXYdMm6nKWSKglqITLCDGmduMGsGIFsGaNZrk/U1Pqnh43DmjVSrf1Y4xVSVUlSDx69Gixxzt16lSm83KQWA4q65fsQeoDuI55D9i8E3KrFGQml3/izDIbO5be5B0dafk+Gxtd14gZorQ0+tCxfDnwLL0EAAoSx42j9cPNzHRWPcZY1VJVgsSKwmMSDUiOIgewoZUsjExydVybfBYuBDp1ouXaOEBkZWVhQUnbL1wATp2i5O0yGXD2LHU/e3jQrPqICF3XlDHG9EpiYiLmz5+P0aNHY/To0Vi4cCGSVD0zZcRBogHJVmSrl+QzlunZJBFTU0qyPWCArmvCqgKJBGjXjtaJjokB5s4FatQAnjyhtcPr1AG6d6cxjTxhijH2kjt37hxq166NhQsX4smTJ3jy5AkWLFiA2rVr48KFC2U+LweJBiRHmQP8R0GYVB9/cnkHzMbFAceP664urOpwcgI+/5xaD3fuBLp1o/3BwTQjunZtmiH96JFu68kYYzry0UcfoVevXrh79y7++usv/PXXX4iMjMSbb76JyZMnl/m8+hhqsCJkK7KBSyMBaMdjeic8nFbU6N2blmpjrDwYGWnS5Ny6BXzyCWBvT79jU6dSV/SwYUBoKMBDrRljL5Fz587hs88+g3Ge3HjGxsaYMmUKzp07V+bzcpBoQHIUOep1m41N9LiLrXZt6hp8+hQYOBDIztZ1jVhVU6cOMG8edUWvWUNpmLKzgd9+A9q3B1q0AH75hSbCMMZYFWdtba1eOzqv6OhoWFmVfZIrB4kGhMYk0qcEE5keJxyWyYAtWwA7O+DMGWDSJE6QzCqGmRkwciRNbDlzhh7L5cDFi8CYMYC7O60bffOmrmvKGGMVZtCgQXj33XexZcsWREdHIzo6Gps3b8bo0aMxePDgMp+XU+CUg8qaQp+rzIVMJiAUJqjROBZ3r7hX2LXKxa5dQK9e9LhrV5qEUK2abuvEqr7Hj6l18aefgDt3NPtff53S6Lz5ph4sV8QY0wdVJQVOdnY2Pv30U6xYsQK5uZT9xMTEBGPHjsV3330HubxseZU5SCwHlflLJpEqASFF3eYxuHneAH6h162jHIoZGYCrK63fW6eOrmvFXgZKJbBvH6Vl+ucfzThFDw/g/feppdHZWbd1ZIzpVFUJElXS09MR8SxFWO3atWFubv5C5+PuZgOiVAIQ9COTyQ2k+3bECOoKbNiQgkMvL13XiL0spFJKk7NrF7UofvYZJXuPiQG++grw9KQ1x48f54kujDGDNmrUKKSkpMDc3BxNmjRBkyZNYG5ujrS0NIwaNarM5+Ug0YCci76kfiwzNaA3tUaNKFD8809NN19WFhAfr9t6sZeHlxelyYmOpmEPbdsCOTnA5s3Aq68CPj60YlBqqq5ryhhjpbZu3TpkZGQU2J+RkYH169eX+bwcJBqQyORbQMvlAAAzMwMKEgFaSSPveMQpUwBfX+DwYZ1Vib2ETE1pFZfQUOD8eVrdxcwMuHKFhkW4uQETJgDXrum6powx9lzJyclISkqCEAIpKSlITk5Wb0+fPsWePXtQ7QXmAnCQaECU0CzLJzeklsT80tIoOHzwAPD3B77+mlfNYJWveXNg1SogNpaWlaxbF0hJAZYto9bvzp2p9TsnR9c1ZYyxQtna2sLe3h4SiQT16tWDnZ2denN0dMSoUaPw4Ycflvn8PMXPgOQocwAFzVCSy/Q5m/ZzWFgAp09Ti83q1cDMmTSh5fffARcXXdeOvWzs7IDJk4GJE4FDh2iiy86dQEgIbW5uwHvv0UQXNzcdV5YxxjSOHDkCIQRee+01bNu2Dfb29upjMpkMNWrUgNsL/N/iINGAJCUJILodAEoFZ9DMzYFff6XWmg8+oHWffXyAjRupdZGxyiaVUpqc11+nsYsrV1JL4/371No9axbQty/w4YdAp056vuwRY+xl0KlTJwBAZGQkPD09IS3nNXv1r7t5+XIaZG5qCrRpQwlyi5KTA3zzDa3wYWpKQUZwsHaZlBRqJahRg8YetW9PkyjyEgKYPp1StJiZUZBy61Z539kLe5JgDETQurWmcv370ZXJO+8A584BjRsDDx/SCi3JybquFXvZeXoC335LweKmTcArr9CQiD//pA82jRpRtzT/rjLG9ECNGjUglUqRnp6O69ev4/Lly1pbWelXpLFlCxAUBMyYAVy4QEFfQAAFD4WZNo0+7S9dSgPNP/iAPulfvKgpM3o0cOAAzWi8coWSOvv70zgkle+/B5YsodmNp09Td2hAAJCZWbH3W0pZ2ZpxiKbyKtSK0aABfRgYMwb4+WfA2lrXNWKMyGTA229TmpxLl+h/jIUFrU8+YQJ1P48dS/9bGGNMRx49eoQ333wTVlZWaNSoEZo1a6a1lZnQJ61bC/Hhh5rnCoUQbm5CzJ1beHlXVyGWLdPe16+fEEOH0uP0dCGMjITYvVu7TPPmQnz5JT1WKoVwcRFi3jzN8cREIeRyITZtKlG1o6OjBQARHR1dovJl9dHatYKaPYV4/5OKvZZeOHhQiH37dF0LxrQlJgqxdKkQDRsK9R8kIETHjvQ/IytL1zVkjJVQZb1/V7QhQ4aIDh06iLNnzwoLCwuxf/9+sWHDBlG/fn2xO38MVAr605KYnU0pKfKOR5NK6XloaOGvycqibua8zMyAEyfocW4udREVVyYyUjPLVsXGhrq6i7tucrJmS0kp+X2+gF51+qsfm5rqz4+uQty/T4mOu3WjFuNnywwxpnM2NsD48cDVqzRLv39/wMiIWhsHDwaqV6dk3dHRuq4pY+wlcfjwYSxYsAAtW7aEVCpFjRo18M477+D777/H3Llzy3xe/Yk0EhIooMu/TJazMwVxhQkIABYsoPGDSiV1K//1FxAXR8etrIB27WjA+f37dP7ffqPgT1VGde7SXHfuXHqjUG3e3mW751IygYX6sZncqFKuqTP29sBbb1EbzezZwGuvaQ8RYEzXJBIan/jHH0BUFA2TcXWlJPHffktjq/v1Aw4efLZcEmOMVYy0tDR1PkQ7Ozs8evQIANCkSRNcuHChzOfVnyCxLBYvptxmDRrQ2KHx44HAQGqBVNmwgQINd3eaErxkCX3af5EZQFOnAklJmq2SEu/mTddmbmbYP7rnMjUFfvqJVsSwsqJWGl/fghOTGNMH7u40AzoqioJGPz8KDLdvp9nSDRsCixYBiYm6rSdjrEqqX78+bty4AQDw8fHBypUrERsbixUrVsDV1bXM59WfSMPRkbps8i/VFh9fdO48Jydgxw5KzhwVBVy/DlhaArVqacrUrg0cPUrLbUVH0wSJnBxNGdW5S3NduZwmV6g2K6tS325ZHI3QdH9X+ZZElUGDaBhCs2bU2ty9OwXp3DLD9JGJCXU/HzlC3dEffkj/H27eBD76iCa6jBkDhIXpuqaMsSpk0qRJiHvWQzpjxgzs3bsX1atXx5IlSzBnzpwyn/fFgsQX6OcuQCYDWrSgZLYqSiU9b9eu+NeamtIn+dxcYNs2oHfvgmUsLKgr6OlTYN8+TZmaNSkYzHvd5GSa5fy861ayWIvdgMt5AIC52UsSJALUWnzqFDBuHD2Pi3uxlmDGKoO3N6XJiY2lVvHGjYGMDOCXX+hDT/v2NPwlK0vXNWWMGbh33nkHI0eOBAC0aNECUVFROHv2LKKjozFo0KAyn/fF3mn//JO+vvLKC51GLSiIkteuW0cpJsaOpVbCwEA6Pnw4tSKpnD5NYxDv3KHuyG7dKLCcMkVTZt8+6qKMjKQxi507U/e06pwSCeVR/PZbWmXhyhW6jpsb0KdP+dxXOTGyfALIaZKMudxEx7WpZKamlENz5076qsLL+TF9Z2VFqXMuX6aVhd5+GzA2prHRw4YBHh70f+3uXV3XlDFWRZibm6N58+ZwdHR8ofO8WJDYsiV1/927R8HazZs0/q+sBg0CfviBElv7+lKXTHCwZlLJvXuaCScA5TGcNo0+sfftS62JJ04AtraaMklJ1OXToAEFf6+8QoGjSZ4ga8oUynn23ntAq1bUNR0cXHBWtI5l5mYCChmAl6wlMa+ePalVGKAPBL1708+P19dl+k4iATp2pOTc0dE0oc7Dg4ZRfPcdDYHp2ZP+9/BwCsbYcwQFBSEtLa3E5adOnYonT56U6hoSIV4gqps7F3jzTQoUBw+mVrhbt2gt1MaNgbVry3xqQxITEwNPT09ER0fDw8Ojwq7z5rKP8M+0SUCSF3btVuDNN17SQFHl4EGaFADQ0IDNmyn9CGOGIjcX2LWL1os+eFCzv3Zt6kkJDKSZ/oyxClFZ798VwcjICA8ePICTk1OJyltbWyMsLAy18s7beI4X725u0oTG2cybR5+AIyKope5Z3zgrP3H/1QGSvAC8xC2Jefn70++gjQ113TVrRm+4jBkKY2PqBTlwgCbeTZpEv88REcAnn1DvSGAgLV3JGGN5CCFQr1492Nvbl2grTaujivEL1VDV3WxhQd3NjRvTJAMHB0oBwcpVdramC0om02FF9Mlbb1Fw+PbbtCZ3r17Axx9TK7fJSzZukxm2+vUpTc7s2dQlvXw5DblZu5a2Vq1o8tagQbQgAGPspbZmzZpSv8Y5f07o53ix7maABmP36MHdzZXQXF1/2DLc/G08AMrk06pVhV3K8GRnA599Rm+yAAWNmzbptEqMvRAhgH//pa7orVvpdxyg7udRo2gyTO3auq0jYwbOkLubK8OL5xFp2pS6Sri7ucJ19gxQP+aWxHxkMmDhQkpe7OxMrYmMGTKJhMbabthAE13mzgVq1ACePKEJfnXr0gf03bt5lj9jrEKUT7I5Jydgzhz6xwVwd3MFMYa5+rFcrsOK6LM+fSjdUcuWmn0nTmhaYRgzRNWqAZ9/Th/Cd+6kdF9CAHv30ozoOnWA//0PeLYUF2NMv/Xt2xd2dnbo37+/et+NGzfg6+ur3szMzLBjxw7dVRLlFST270+Boap//MoV+ofGylUWj0ksmbzjtS5eBLp0odRHkZG6qxNj5cHIiILCvXtpaM/HH9Pwnrt36X+uhwel+vr33xdLR8YYq1CTJk3C+vXrtfbVr18fYWFhCAsLw4kTJ2BhYYHXVRk8dKR8gsS0NOD99zWRS5Mm1OXMytWV2Bvqx9ySWEKPHtHEqrNnaYLL9u26rhFj5aNOHeq9iY0FVq+mFauys6l7ul07ev7LL0B6uq5ryhjLx8/PD1bFLOm7c+dOdOnSBRaqvMA6Uj5BorMzcP8+jaFRycwsl1Mzjbgai9WPuSWxhLp2pdbEtm0psXq/fsA779CklthYXdeOsRdnZqZJk3PmDI0Hl8vp937MGEqj89FHtNgBY+yFHTt2DD179oSbmxskEkmhXcLLly+Hl5cXTE1N0aZNG5w5c6ZU19i6desLLaeXnJyMHTt2IDw8vMznAMorSFy0iP4xPXwIbNlC/7AaNCiXUzMNqdMt9WNuSSyFGjVoObRPP6XnGzcCQ4bQCjt53b7NXXTMsLVqRcN+YmNpMmGtWkBiIv2Prl+fPjTt2EFJvBljaikpKUhOTlZvWcWsqZ6WlgYfHx8sz7tEbB5btmxBUFAQZsyYgQsXLsDHxwcBAQF4+PBhieqSnJyMU6dOoUePHiWu/8CBA7Fs2TIAQEZGBlq2bImBAweiadOm2LZtW4nPk1/ZgsT8N1qnDs2wW7AA+O8/mjSwcWOZK8UKl52taanllsRSMjEBvv+e1viePJm64jp31hyPiaHZoi4uNMZ28WLgwgWeNcoMk4MDJeO+dQvYs4dWxpJIKBNF374UPM6eDcTH67qmjOkFb29v2NjYqLe5c+cWWbZ79+749ttv0bdv30KPL1iwAGPGjEFgYCC8vb2xYsUKmJubY/Xq1SWqy99//42uXbvCtBRLAx87dgwdO3YEAGzfvh1CCCQmJmLJkiX49ttvS3ye/MqWTLt/f+DIERpErSKTAQMHUnee8Yvl6GaFS7/TVP2Yg8QyeuUV2vILD6fm2YcPgW3baAMAKyugQwcKLAMCCr6OMX0mldKCB92708StlStpnGJ0NK17P3MmJaT/8EP6Pc87ZIixl8i1a9fg7u6ufi4vY3dddnY2zp8/j6lTp6r3SaVS+Pv7IzQ0tETn2Lp1K97L39P1HElJSbB/toRncHAw3nrrLZibm+ONN97Ap6petDIoW0uirS0wcWLB/Y8f01JprEKknx5MDyQKSMtnoABTef11GrN4/Dilc+reHbC2BlJSKP9nYqKm7KVLwBdf0AzTpCSdVZmxUqlZE/juO2o1X7+exunm5NCa5x07Ar6+FESmpuq6poxVOisrK1hbW6u3sgaJCQkJUCgUBVY2cXZ2xoMHD9TP/f39MWDAAOzZswceHh7qADIpKQlnzpxBQCkbJTw9PREaGoq0tDQEBweja9euAICnT5+WqkUyv7KFGuvXU7dF3qbT8HCgdWuaScoqhDKHlpmTGHEXaIWQy6mVcepU6qJ78oQG/y9Zot01vWcPJTbu0YNWv2jWjNbc/fNP7r5j+s/UFBg2jNY7P38eePddmvxy+TKt4uLmBkyYQP/TGWMV4uDBg3j06BHS09MRExODdu3aAQBsbGwQHx8PWSm7CydPnoyhQ4fCw8MDrq6u8HuWq/rYsWNo0qRJmetZ9pbEbdtoIsCZM5Tupl07SmS8a1eZK8OKp8ylTzYSqfI5JVm5MDKi1pUJEyiZsUqLFsCIETSuS6mk9XWXLAEGDKAxjRcvaspmZvJkGKa/mjen7ufYWFqxqG5daj1ftgzw9gZee40+/OTk6LqmjBkER0dHGBkZIT5fg0F8fDxcXFwq7Lrjxo1DaGgoVq9ejZMnT0L6rLuxVq1aLzQmseRBYr9+wDffULb/e/coF+KyZdSa8tZb9A9m/nxwP2jFcTf3AgBIjThI1KmuXWld8ogIenPdvJnGdDVtClha0rrlKhMnUoLjt98Gli+n1hol//yYnrGzo3G3168D+/fTB36plMaeDxgAeHnR+MX793VcUcb0m0wmQ4sWLXDo0CH1PqVSiUOHDqlbCytKy5Yt8cYbbyA2Nha5zzIYvPHGG+jQoUOZz1nyiK52bRqvNXo0/cNwcABWraJWkiFD6BMpf9qsUBIFrSRiZMzdzXrDzQ0YNIg+MF26BDx4QDOpVf79l95Yt2wBxo8HfHzob+fNN2m2NQeMTJ9IpTQ+d/t2WsXlyy+pFf3+feDrrymd1MCBQEgIt5Czl1Zqaqp6ZRQAiIyMRFhYGO7duwcACAoKwqpVq7Bu3TqEh4dj7NixSEtLQ2BgYIXVKT09He+++y7Mzc3RqFEjdV0mTJiA7777ruwnFmUREyPE7t1CfPutEP37C1GnjhBSqRAymRBNm5bplIYsOjpaABDR0dEVeh23ejECEMLMNrFCr8PKUXq6EEeOCPHNN0K8/roQFhZC0NurEHXrapd98kQnVWSsWFlZQvz+uxCvvKL53QWE8PYWYtkyIZKSdF1DxsqsLO/fR44cEQAKbCNGjFCXWbp0qahevbqQyWSidevW4t9//62A2mtMnDhRtGjRQhw/flxYWFiIiIgIIYQQO3bsEL6+vmU+r0SIcvo4mJpKY7MuXaKut5dITEwMPD09ER0dDQ8Pjwq5hhACVtXvIC2mNiwcnyD1kX2FXIdVsNxc+js5dowmyqj+VrKyAE9P6qr+4APq7uM8R0zfXL4M/Pgj8NtvtBwrQJMVhw0Dxo2jYUiMGZDKeP+uDDVq1MCWLVvQtm1bWFlZ4dKlS6hVqxZu376N5s2bIzk5uUznLb8BhJaWNDP0JQsQK0uOMgdpdWk2udyclzw0WMbGlGw+KEj7byU0lFJIHTlC3dfVq1NXX1SU7urKWH5NmwIrVtBY3KVLgYYNKVhcsYKOvfoqjdHNztZ1TRl7qTx69AjV8k6wfCYtLQ2SF8h/yrNMDESOIgdwvgIAkFvw2M8qx8+Pkh1/9RXNkI6Pp3yNNWvS+MUrV3RdQ8Y0bGxojO3Vq8Dhw7TAgpERjVsfPJg+5Hz1FeVkZIxVuJYtW+Kff/5RP1cFhr/88ssLTZjhINFAZCuyAQWlwDEx4ckOVVL16pRB4N49SjvSpQuN/vrnH+3VjXjCANMXEgnlEP3jD2r1njEDcHWlDznffkuTHPv1Aw4e5N9bxirQnDlz8MUXX2Ds2LHIzc3F4sWL0bVrV6xZswazZ88u83k5SDQQOcocIK4ZAMDIiJfOqtJMTCit1MGDwI0b1K3n7a05PmIEdUkfOcJvvEx/uLvTDOioKGDrVmodVyhopvTrrwMNGtCa6HlXL2KMlYtXXnkFYWFhyM3NRZMmTbB//35Uq1YNoaGhaNGiRZnPW34TV15ilTHwNTY5Fh4uMiDDCV5NYhB52XAH2LIX8OQJdUer0k3Vr08TXUaMoFx3jOmTa9doosv69ZSkGwDMzYGhQ2mii6+vTqvHWFWZuFJRuCXRQGQrsgGlMQDARMZx/UvL3h44e5YCQ0tLamn86CPK1xgYSDOnGdMX3t6UQzQ2FvjpJ5q9n55OOXabNQPatwc2bqTZ/YyxUklOTi7xVlYcJBqIHGWOOkiUcZD4cvPxoTfc+/fpa9OmtPzf2rVAniz/jOkNKyv6YHP5MqV/GjSIZvqHhgLvvEPpn6ZO5dn8jJWCra0t7OzsSrSVFQeJBsLT2hMS0EoeclOeuMKgeeMNCwNOnaIu5xEjNMe3baMZqP/9p7MqMqZFIgE6dqQ0OdHRwKxZtGzlo0fAd9/RbP5evYDgYF6NiLHnOHLkCA4fPozDhw9j9erVqFatGqZMmYLt27dj+/btmDJlCpydnbF69eoyX4PHJJaDyhrTIDXOgVCYoG23uwjd61Vh12FVxCuvACdPah6PHUsTYuRy3daLsbxyc4Fdu2js4sGDmv21a9PvbGAgDbNgrAJUlTGJXbp0wejRozF48GCt/b///jt+/vlnhISElOm83JJoQISS0qCYmnJcz0pg5kwKCo2MgBMnaLKAhwcwZQoQEaHr2jFGjI2Bvn2BAweA69eBSZMoD2NEBPDJJzRretQo4Nw5XdeUMb0VGhqKli1bFtjfsmVLnDlzpszn5SDRQEQnxQCCflympjquDDMMXbpQvsV79yj/oocHkJAAzJsHjBmj69oxVlD9+sCiRTTR5eefafxtZiawZg3QqhXQpg2wbh2QkaHrmjKmVzw9PbFq1aoC+3/55Rd4enqW+bwcJBqIGwk3gZo0KcHSklsSWSm4udHqF5GRwN9/A927UzeeysOH1OoYG6u7OjKWl4UFfZC5eJHG277zDq1lfuYMMHIkfeD59FPgzh1d15QxvbBw4UIsXboUTZo0wejRozF69Gg0bdoUS5cuxcKFC8t8Xg4SDYRC5AAONwAA5uacTJuVgbExTQrYswcYMECz/9dfKQlyjRq0Osb+/TxpgOkHiQRo1w7YsIEmusydS7+nT54AP/wA1KkD9OhBqxIpFLquLWM606NHD9y8eRM9e/bEkydP8OTJE/Ts2RM3b95Ejx49ynxe43KsI6tAtCyfDABgZspBIitHTZsCr75KqUm2b6etdm3g/fdp0oCjo65ryBhQrRrw+efUgrhnD010CQ4G9u6lzcuLZvu/+y7/zrKXkqenJ+bMmVOu5+SWRAORnqkAEmsAAEzl/GNj5eiNN4CjRylVzoQJgLU1TRqYMoVaanj8F9MnRkZAz54UGN66BXz8Ma02dPcuBZEeHsDw4cC///KylaxKu3z5MpTPen0uX75c7FZWHG0YiIRHUiDydQCAmSn/2FgFaNQIWLKEknT/8gvQogV1T5uZacps3Qq8QPZ+xspVnTrU7RwbC6xeTb+zWVnUPd2uHT3/9VcgNVXXNWWs3Pn6+iIhIUH9uFmzZvD19S2wNWvWrMzX4O5mA5GZpRkjZio30mFNWJVnYUFddu++q92KePkyrZRhYUHpdD74gJZWY0zXzMxoaERgIC1buXw5Jey+eBEYPRqYPJnG4QYGUs5QCQ/ZYYYvMjISTk5O6scVgZukDERmlmZQtrkZB4mskuRtRXz8GGjYEEhLo/QkzZsDbdvScoDcJc30RatW9DsZG0vpnurUoZbENWto7G3dusC331JqKMYMWI0aNSB59oGnRo0axW5lxSuulIPKyNh+4FQ8unZwBgAs/fUxxo9yqJDrMFYsIWiCy08/AX/9BeTk0H5bWyAkhPLaMaZPhKCVh9asoeESqq5niYRyiY4cScm8zc11Wk2mG4a84srOnTtLXLZXr15lugZ3NxsIO1k19WMLU/6xMR2RSIBOnWiLj6dxYCtXUsLjhg015a5fB2rVotx2jOmSREJdzK+8QmNut22jlsYjR2gZwIMHabLW229TwNi2LXdHM4PQp0+fEpWTSCRQlDFFFHc3G4j0zFz1YzNT7m5mesDZGZg6lWZCHz+uCQgVCiAgAKheHfjySyAqSrf1ZEzFwoJmPh8+TIm4v/6aUuckJ9MQivbt6cPOd99xcnmm95RKZYm2sgaIAAeJBuPy/evqx5ZmJjqsCWP5GBnROC+VO3eA7GxqaZwzB6hZE3jzTU54zPRLzZrAjBn0IefIEQoezc2BGzfow0/16rQ60dat1FLO2EuIg0QDEZ77D2ARDwAw5+5mps/q1qVJAX/+SWO+hKAA8c03KUn37t26riFjGlIp4OdHa0I/eEApc155hVYdCg6mGf1ubsCHHwLnznHuRaaXJk6ciCVLlhTYv2zZMkyePLnM59W/IHH5cmr+NzWlxdzPnCm6bE4O8M039MZjakqD5oODtcsoFLRubc2aNFOzdm1g1iztP/SRI2kMSt6tW7eKuLsyk9nHAzIacM2zm5neMzEB3nqLxnvduAEEBVHC46go7dUw0tL4TZfpDysrYNQoGj5x6xYNl/DwAJ4+pRVeWrUCmjQB5s+ngJIxPbFt2zZ06NChwP727dvjzz//LPN59StI3LKF3kxmzAAuXKCgLyAAePiw8PLTptGg+aVLgWvXKG9b376UG0vlf/+jmZjLlgHh4fT8++/pNXl16wbExWm2TZsq7j7LIDM3E8iVA+C5AMzA1KtHb6qxsTRpoE0bzbFPPgG8vYHFi+mNmDF9UacOpcq5e5fWMx88mBojrl6l31sPD0o2/9dfNLyCMR16/PgxbGxsCuy3trZWJ9wuC/0KEhcsAMaMoYSn3t7AihU0RmT16sLLb9gAfPEFLfBeqxYwdiw9nj9fU+bUKaB3b1p6zMsL6N8f6Nq1YAulXA64uGg2O7ui65mVRQOdVVtKygvf+vMkPZYBOebqqjJmcMzMgH79NDNHc3OBv/+mmdCTJwPu7tSKc+YMty4y/WFkBLz+OvD779SAsGIFzYBWKIBdu6jF3N2dfofDwnRdW/aSqlOnDoLz96QC2Lt3L2rVqlXm8+pPkJidDZw/D/j7a/ZJpfQ8NLTw12Rl0Se7vMzMgBMnNM/btwcOHQJu3qTnly7R8e7dtV8XEkILyNevT8Hm48dF13XuXMDGRrN5e5f4Nsvq7oV6QKY9AG5JZFWEsTF1Rf/0E9C0KSXkXrOGWhpbtKCeBcb0ia0t8P779J507Rrw2WeAqyuQkECt4c2a0bZ4Me1jrJIEBQVhypQpmDFjBo4ePYqjR49i+vTp+Pzzz/HRRx+V/cRCX8TGCgEIceqU9v5PPxWidevCXzN4sBDe3kLcvCmEQiHE/v1CmJkJIZNpyigUQnz2mRASiRDGxvR1zhzt82zaJMTffwtx+bIQ27cL0bChEK1aCZGbW/h1MzOFSEpSb9HXrgkAIjo6usy3/zzNP1gqqHlFiKioCrsMY7qhVNLf/rBhQsjl9Is+bZqua8XY8+XkCLFnjxADBtB7j+oftYmJEH37CrFzpxDZ2bquJStCdHR0hb9/V5Yff/xRuLu7C4lEIiQSiahZs6ZYt27dC51Tf1oSy2LxYppJ2aABNa+NH09d1dI8t7V1K7BxI3UVXLhAM9h++IG+qrz9No0tadIE6NOHZl+ePUuti4WRyyn5qmqzsqrIuwSgPeSFWxJZlSORAO3aAevX09jFH34A3ntPc3z3bqBjR/o7zsrSXT0Zy8/YWJMqJy6Oxr+3aEETK7dvp/cWT08ax3j1qq5ry6qwsWPHIiYmBvHx8UhOTsadO3cwfPjwFzqn/gSJjo409iM+Xnt/fDyNESyMkxOwYwfNkIyKorFNlpY0PlHl00+Bzz+nQLBJE2DYMOCjj6jLuCi1alF9bt9+4dsqL43sm6sf85hEVqU5OAAff0xvrCorV9IwkaFDacLAZ59RfjvG9Im9vSZVzuXLNBGzWjV6H5s/H2jcmGZI//gj8OSJrmvLqignJydYWlqWy7n0J0iUyejT16FDmn1KJT1v167415qa0sDh3FyaPdm7t+ZYerp2yyJAwahSWfT5YmJoTKKra+nvo4JYwEn9mFsS2Utn5Upg5kz6O09IoAwFdepQVoIdO3iiC9M/qlQ5MTE0QatvX2p1PHeOAklXV8rBuHcvJ5lnL6xmzZqoVatWkVtZ6VdW5qAgYMQIoGVLoHVrYNEiaiUMDKTjw4fTm4SqFfD0aeqa8vWlr19/TcHflCmac/bsCcyeTdnzGzWi9DgLFtAsSoAWe585k2aoubhQ68SUKfQGFBBQiTdfvIwMTVDLLYnspePmBkyfTtkM/vmHZpju20fbw4faHwwZ0ycmJtTl3KsX8OgRDX9as4ZaGrdupc3Njd7fRo6kyZOMlVL+hNk5OTm4ePEigoOD8emnn5b9xOU0XrL8LF0qRPXqNAC4dWsh/v1Xc6xTJyFGjNA8DwmhSSZyuRAODjToPTZW+3zJyUJMmkTnNDUVolYtIb78UoisLDqeni5E165CODnRQOMaNYQYM0aIBw9KXOXKGPja/u0Tz8ZDK4VSWWGXYcxwRETQpLTff9fsS0oSYtAgIfbto0lrjOmrCxeEmDiR3rtUk10AIdq1E2LlSiESE3Vdw5dCVZq4Uphly5aJkSNHlvn1EiG4n+ZFxcTEwNPTE9HR0fDw8KiQa9QYE4R7vywAjLIhcrm/mbFC/fgjdeUBtLrS++9TT0TeVV4Y0ydZWdQ6vmaNdtezqSnlFQ0MBF57reCwKVYuKuP9uzB9+/ZFSEgIunTporUiipeXF6ytrSGVSmFnZ4cjR4680HXu3LkDX19fJCcnl+n1+tXdzIpk4hYOAJCaZAHgIJGxQnXpQlkO1q/XDB2ZNg0YMIDyn7Zvr0nmXQUpFArk5OTouhqstHr0oO3RI0rQ/ddfNHHy5EnaXF0p80bfvjR0ipWKTCaDVM+C7EmTJmHUqFFYlzfTyjOnTp0qt4knf/75J+zt7cv8eg4SDUT2s//7UhN+A2CsSPXr05Kb331HS2v+9BOlvtq4kZ7fu0fjmqsYIQQePHiAxMREXVeFvajOnWnLyqIx8+npmomWd+7Q+HtLS1qNTM8CH30llUpRs2ZNyPRo1qefnx9CikqzVwbNmjWDJM8HYNX/hEePHuHHH38s83k5SDQQmQnVAAASaTGzshljxMICGD2atrNnaaJLerp2gLhkCeVebNZMd/UsJ6oAsVq1ajA3N9d6s2AGTqmk5V+fPqWJnCqZmbTil50dBYz8My+UUqnE/fv3ERcXh+rVq5fL38axY8cwb948nD9/HnFxcdi+fTv69OmjVWb58uWYN28eHjx4AB8fHyxduhStW7d+7rklEgk6deoEqVSKyZMnY+jQoSWqU/7rS6VSODk5wc/PDw0aNCjprRXAQaKBSD41GAAgcvTnkxBjBqFVK9ryDr+OjKS1doWgZQA/+IDSkZiZ6ayaZaVQKNQBooODg66rwyqCuTll38jOpvRsjx9TkJiYSJtcTvlFHRw4/UUhnJyccP/+feTm5sLExKTQMikpKVrj9uRyOeRFfC/T0tLg4+ODUaNGoV+/fgWOb9myBUFBQVixYgXatGmDRYsWISAgADdu3EC1atWKreuJEyfg7u6OuLg4+Pv7o0mTJmjatOlz73HGjBnPLVMW3FZtIEQu/WJLjbglkbEyyduCkJsLDBxI6UlOn6bJAW5ulGj/xg3d1bEMVGMQzc3NdVwTVuFkMhqf2KgRrTTm6EhdzllZwP37wJUr9Pv7+DHnXsxD1c2sKOZ74u3tDRsbG/U2t5gFN7p3745vv/0Wffv2LfT4ggULMGbMGAQGBsLb2xsrVqyAubk5Vq9e/dy6uj/r7XB1dUWPHj1w4cKF576mInGQaCCUz4JEiRH/4TP2wurWBTZvBqKjgTlzgBo1qEVm0SJ68925U9c1LDXuYn6JSCQ0LtHLC/DxAWrW1CwPm5JCLeWXLgF379K4xpc8iUlJ/jauXbuGpKQk9TZ16tQyXSs7Oxvnz5+Hv7+/ep9UKoW/vz9CQ0OLfW1aWhpSUlIAAKmpqTh8+DAaNWpU7GukUimMjIyK3YyNy95pzN3NBsLWxAkJAIxNXu4/dsbKlbMzMHUqzYLet4/GLp44QbOkVU6fptYbnlXK9JGRkaarOStL0x2dlUWrEyUkUBe0oyOV0aPJG/rEysoK1tbWL3yehIQEKBQKODs7a+13dnbG9evX1c/9/f1x6dIlpKWlwcPDA3/88QecnZ3VrZMKhQJjxoxBq1atir3e9u3bizwWGhqKJUuWQFncCnPPwUGigTBWWtBX/okxVv6MjDRpSFJSaOILQC0w774LhIfTsbFjaSUmIyPd1rcK8PPzg6+vLxYtWlTmc3z99df46aef8PDhQ/XkgcL2vTTkcho24epKLYgJCTThJSuLZkXHxgLW1hQw2tqWeHb03bt3UbNmTVy8eBG+vr4Vegsvi4MHDxa6/9KlS6U6T+9CVpu6ceMGPv/8c+zatQtDhw7FN998U6Y6AtzdbDByc+hHZWzCYxIZq1CqbjuAuqBdXGiG6e7dwBtvUJLuOXOA+HidVZEB4eHhmDlzJlauXIm4uDh079690H0vJYmEfo9r1qTuaC8vze91cjKl0rl0CYiK0nl39Ndff12lAk9HR0cYGRkhPt//h/j4eLi4uFTote/fv48xY8agSZMmyM3NRVhYGNatW4caNWqU+ZwcJBqIjGwaiyg15jGJjFUaOzvg4EHg+nVaW97Ojt5Yv/wS8PAA5s/XdQ1fWhEREQCoJcXFxQVyubzQffpApwnOjYyo5bB+faBxY2pllMloYsujR/S7ffUq8OABzZ4uJ9nleC5DIpPJ0KJFCxw6dEi9T6lU4tChQ2jXrl2FXDMpKQmfffYZ6tSpg6tXr+LQoUPYtWsXGjdu/MLn5iDRQKQ7HAMAyC3TnlOSMVbu6tengDA2Fli3DmjblmZI5/0n/Pgxde2xMvnnn39gY2ODjRs3AgCio6MxcOBA2Nrawt7eHr1798bdu3cBUOtTz549AdDAfYlEUui+wrRs2RI//PCD+nmfPn1gYmKC1NRUALRMm0Qiwe3btwEAT58+xfDhw2FnZwdzc3N0794dt27dKvZeJBIJfvrpJ/Tq1QsWFhaYPXs2AODvv/9G8+bNYWpqilq1amHmzJnIzc3Vet3KlSvx5ptvwtzcHA0bNkRoaChu374NPz8/WFhYoH379upgWOWnn35C7dq1IZPJUL9+fWzYsEF9bMiQIRg0aBA9MTUF3N2R06ABHAMCsD4kBJBKEXz4MF7p1g22Dg5wsLPDmwEBiHjOPebn5eWFWbNmYfjw4bC2tsZ7770HAPjss89Qr149mJubo1atWvjqq6/UQfPatWsxc+ZMXLp0CRKJBBKJBGvXrgUAJCYmYvTo0XBycoK1tTVee+01ra7YS5cuoXPnzuqxhC1atMC5c+dKVeeySk1NRVhYGMLCwgAAkZGRCAsLw7179wAAQUFBWLVqFdatW4fw8HCMHTsWaWlpCAwMLPe6fP/996hVqxZ2796NTZs24dSpU+jYsWP5XaCc1pB+qVXGAuHoPl4AQjTsdLnCrsEYK4VLl4RQKDTPP/tMCDMzIQIDhThzRgilslKqkZGRIa5duyYyMjIKHkxNLXrLX764sunpzy9bSp06dRKTJk0SQgixceNGYWVlJXbt2iWEECI7O1s0bNhQjBo1Sly+fFlcu3ZNDBkyRNSvX19kZWWJlJQUsWbNGgFAxMXFibi4uEL3FSYoKEi88cYbQgghlEqlsLe3F46OjmLv3r1CCCF+++034e7uri7fq1cv0bBhQ3Hs2DERFhYmAgICRJ06dUR2dnaR9wZAVKtWTaxevVpERESIqKgocezYMWFtbS3Wrl0rIiIixP79+4WXl5f4+uuvtV7n7u4utmzZIm7cuCH69OkjvLy8xGuvvSaCg4PFtWvXRNu2bUW3bt3Ur/nrr7+EiYmJWL58ubhx44aYP3++MDIyEocPHxZCCLF7925hZmYmUlJS1K/ZtWuXMDMzE8nJyULk5Ig/f/1VbFu8WNz66y9x8bffRM+OHUWTunWFIjJSiNRUEXnnjgAgLl68WOQ916hRQ1hbW4sffvhB3L59W9y+fVsIIcSsWbPEyZMnRWRkpNi5c6dwdnYW//vf/4QQQqSnp4uPP/5YNGrUSP0zS3/2u+bv7y969uwpzp49K27evCk+/vhj4eDgIB4/fiyEEKJRo0binXfeEeHh4eLmzZti69atIiwsrNC6Ffc3Upb37yNHjggABbYRI0aoyyxdulRUr15dyGQy0bp1a/Hvv/+W+PylIZFIhLm5uejVq5fo27dvkVtZcZBYDio6SFQqlQJdgwQghM/rHCQyppe6dBGCRnfR1ry5EKtWlSmAKo1ig8S89cm/9eihXdbcvOiynTppl3V0LFimlFRB4rJly4SNjY0ICQlRH9uwYYOoX7++UOYJtLOysoSZmZnYt2+fEEKI7du3i/ztHIXty2/nzp3CxsZG5ObmirCwMOHi4iImTZokPvvsMyGEEKNHjxZDhgwRQghx8+ZNAUCcPHlS/fqEhARhZmYmtm7dWuQ1AIjJkydr7evSpYuYM2eO1r4NGzYIV1dXrddNmzZN/Tw0NFQAEL/++qt636ZNm4Spqan6efv27cWYMWO0zjtgwADR49nPNycnRzg6Oor169erjw8ePFgMGjSoYMUzMoSIjhaPngVBVzZtEuLsWRG5bx8FiWfOFHnPNWrUEH369CnyuMq8efNEixYt1M9nzJghfHx8tMocP35cWFtbi8zMTK39tWvXFitXrhRCCGFlZSXWrl373OvRbZVvkKhPRowYIUaOHPncrax4rqwBUAgFkErT6U2MeYQAY3rpwAEgNJTWi/7jD1ozeswY4OOPgfHjgWddjkzjzz//xMOHD3Hy5EmtVB+XLl3C7du3YZV3EhGAzMzMAl2tpdWxY0ekpKTg4sWLOHXqFDp16gQ/Pz989913AICjR4/i008/BUCTY4yNjdGmTRv16x0cHFC/fn2Eh4cXe52WLVtqPb906RJOnjyp7noGKM1JZmYm0tPT1cnQ866uoUqj0qRJE619mZmZSE5OhrW1NcLDw9VduyodOnTA4sWLAQDGxsYYOHAgNm7ciGHDhiEtLQ1///03Nm/erC5/69YtTJ8+HadPn0ZCQoI6Zcq9tDQ0lkhodRcqSONyHR1plnS+2dH57xmg1UeWLFmCiIgIpKamIjc397mpZi5duoTU1NQCKwhlZGSof/5BQUEYPXo0NmzYAH9/fwwYMAC1a9cu9rxVkap7vqJwkGgAshXZwEUay5CdZnjLhjH2UpBIgPbtaVu4kMYurlgB3L5NaXVUhKAJApUxqeLZOLtC5U/j8/Bh0WXzp0p5NjbwRTVr1gwXLlzA6tWr0bJlS/U4wtTUVLRo0UI9PjEvJyenF7qmra0tfHx8EBISgtDQULz++ut49dVXMWjQINy8eRO3bt1Cp06dXugaAGChSqP0TGpqKmbOnFnoMm6mpqbqx3mXjVN9PwrbV5rcd0OHDkWnTp3w8OFDHDhwAGZmZujWrZv6eM+ePVGjRg2sWrUKbm5uUCqVaNy4MbIdHGh2tGr2sxCapQCNjTX5GZ8FuPnvOTQ0FEOHDsXMmTMREBAAGxsbbN68GfOfM+ErNTUVrq6uCAkJKXDM1tYWAI1LHTJkCP755x/s3bsXM2bMwObNm4tcBYWVDQeJBiBHkQM8a/Q1NeVVFRjTe46O1IL40UfA4cNArVqaYydOAP36AaNGAe+9Ryl1Kkq+N22dlC1G7dq1MX/+fPj5+cHIyAjLli0DADRv3hxbtmxBtWrVyiXBcX6dOnXCkSNHcObMGcyePRv29vZo2LAhZs+eDVdXV9SrVw8A0LBhQ+Tm5uL06dNo3749AODx48e4ceMGvL29S3XN5s2b48aNG6hTp0653kvDhg1x8uRJjBgxQr3v5MmTWvVr3749PD09sWXLFuzduxcDBgxQB56q+1m1apV6wsOJEyc0FzA2pt9ngH5XnZ2BJ0+AnBxKAxUfT0GiUllgKcBTp06hRo0a+PLLL9X7oqKitMrIZLICy+U1b94cDx48gLGxMby8vIq893r16qFevXr46KOPMHjwYKxZs4aDxHLGfZcGIEeZAyjpU3+eD5yMMX0nlQL+/tpB4qZNlOT4+++BOnWAbt2AHTtotvRLqF69ejhy5Ai2bduGyZMnA6CWL0dHR/Tu3RvHjx9HZGQkQkJCMHHiRMTExLzwNf38/LBv3z4YGxujQYMG6n0bN27UakWsW7cuevfujTFjxuDEiRO4dOkS3nnnHbi7uxeaxLg406dPx/r16zFz5kxcvXoV4eHh2Lx5M6ZNm/ZC9/Lpp59i7dq1+Omnn3Dr1i0sWLAAf/31Fz755BOtckOGDMGKFStw4MABDB06VL3fzs4ODg4O+Pnnn3H79m0cPnwYQUFBhV/M1BTw9ASaNqXfXTs7akFPT6ff3/v3gYgIamkUAnXr1sW9e/ewefNmREREYMmSJQVWCPHy8lLPDk5ISEBWVhb8/f3Rrl079OnTB/v378fdu3dx6tQpfPnllzh37hwyMjIwfvx4hISEICoqCidPnsTZs2fRsGHDF/pesoI4SDQAFiYWkAj61MctiYwZuCVLKCgMCKDn+/YBfftSwuOZMzXjv14i9evXx+HDh7Fp0yZ8/PHHMDc3x7Fjx1C9enX069cPDRs2xLvvvovMzMxyaVns2LEjlEqlVkDo5+cHhUIBPz8/rbJr1qxBixYt8Oabb6Jdu3YQQmDPnj1aXcAlERAQgN27d2P//v1o1aoV2rZti4ULF75QomOAUvgsXrwYP/zwAxo1aoSVK1dizZo1Be5j6NChuHbtGtzd3dGhQwf1fqlUis2bN+P8+fNo3LgxPvroI8ybN6/4i0oktGJL7doUMHp60j4hKA3U7dvA5cvo1bw5PpowAePHj4evry9OnTqFr776SutUb731Frp164bOnTvDyckJmzZtgkQiwZ49e/Dqq68iMDAQ9erVw9tvv42oqCg4OzvDyMgIjx8/xvDhw1GvXj0MHDgQ3bt3x8yZM1/oe8kKkgjxkq/8XQ5iYmLg6emJ6OhoeHh4VMg1pCZZELly9B5xFzvWelXINRhjlSwiAvj5Z2D1ampdrF0buHmzxMulATSZIzIyEjVr1tQa28ZYpUtP16wdnbdl3MKCxi7a2+tkbdni/kYq4/3bkPGYRAMhuLuZsaqndm3gf/8DvvkG2LaNJpOoAsSsLMDPj8YvBgZqxoUxpq/MzWlzdweSkihYTEwE0tJoi46mLmoHB5odXUTCc6Y/OEg0AClZKYCg2WPm5vxHxViVI5cDQ4Zo79u2Dfj3X9q++goYMAD44AOaPc1vrkyfSaUUDNrZ0QQXVetiRgZNennyhJYGVM2O5tYPvcVjEg3Anad3ALs7AABrq+cUZoxVDb17A6tWAc2bU6vib78Br7xCKUl+/FE7rQ5j+srEBHBxAby9gYYNgWrVqMU8OxuIiwP++4/Wj370qMDsaKZ7HCQagGxFNuB4HQBgZ8s/MsZeChYWwOjRwPnzwJkz1OVsZgZcuQJ8+CFQDrN8Gas0Egn9TlevTh90atUCbGzoWGoqEBUFXLoEREYCycma3IxMp7i72QDkKHOAXEq8ayrnIJGxl06rVrTNnw+sX0+BYt50H4mJNKvU2blUk14Y0wmplCax2NtTi6KqOzozU/NY1R3t6Fg5iedZoThINACZ2TlANvUzm5saPac0Y6zKsrMDJk3S3peQQJMEYmOBBw/oTdXJicd5McMgkwGurtQlnZZGAeKTJ5ru6Lg4wMqKAkY7u4IrBbEKxUGiAUh4LICYdgAAc1P+kTHG8pDJKGediYn2KhhWVhQs2tpy6yLTfxIJYGlJm6cntYw/fkxdzykptMXGUl5GnrhVaTjiMAAZmZrBvBbm/CNjjOVhbU1ju7y8qPXl4UNqWVS9sXp6Ujc0Y4ZCKtXMfM7KopbFhAQKIDlArFQccRiA1AzNCgzcksgYK5REQsGijQ29sSYkUEuMg4OmTEoKrbHLOeqYoZDLNd3RSqWua/PS4T4IA+BpoVkQ3sKMg0TG2HPI5ZTQuEkT7RUuYmOBW7do4ktcHHVP64ifn596reay+vrrr+Hs7AyJRIIdO3YUuY8Vb+TIkejTp4+uq1E8iYTHI+oARxwGwMHURf2YWxIZYyWWt7VQCEpBkpFB3dKxscD9+zRmsVo1g+vKCw8Px8yZM7F9+3a0bdsWdnZ2he5juiWRSLB9+3b9D0JZoTjiMABpmZpP+9ySyBgrE4mExie6u9MYr0ePaDbp06e0OTgANWvqupYlFhERAQDo3bs3JM+C28L2VbTs7GzIZLJKuZa+UCgUkEgkkPKEqCqPf8IGIPpJvPrxS/a/iDFW3qRSSpPTsCGtguHkRPtUiY0B6oZOS6vUhMb//PMPbGxssHHjRgBAdHQ0Bg4cCFtbW9jb26N37964e/cuAOpS7tmz57PbkUIikRS6rzAtW7bEDz/8oH7ep08fmJiYIDU1FQAQExMDiUSC27dvF/r6r7/+Gr6+vvjll19Qs2ZNmD5LNZSYmIjRo0fDyckJ1tbWeO2113Dp0qUCr1u9ejWqV68OS0tLjBs3DgqFAt9//z1cXFxQrVo1zJ49W+t69+7dQ+/evWFpaQlra2sMHDgQ8fH0nnDz5k1IJBJcv35d6zULFy5E7dq1AVBA9+6776JmzZowMzND/fr1sXjx4uf8NLStXbsWtra22LlzJ7y9vSGXy3Hv3j2cPXsWr7/+OhwdHWFjY4NOnTrhwoUL6td5eXkBAPr27QuJRKJ+DgB///03mjdvDlNTU9SqVQszZ85Ebm4uAEAIga+//hrVq1eHXC6Hm5sbJk6cWKo6s/LBQaIBCEs+AEipNZFzijLGnksICvKetwlBAWOdOpRCR7X/3j3gwgVa7eXuXUpDUtLzlcHvv/+OwYMHY+PGjRg6dChycnIQEBAAKysrHD9+HCdPnoSlpSW6deuG7OxsfPLJJ1izZg0AIC4uDnFxcYXuK0ynTp0QEhLy7NskcPz4cdja2uLEiRMAgKNHj8Ld3R116tQp9PUAcPv2bWzbtg1//fUXwsLCAAADBgzAw4cPsXfvXpw/fx7NmzdHly5d8OTJE/XrIiIisHfvXgQHB2PTpk349ddf8cYbbyAmJgZHjx7F//73P0ybNg2nT58GACiVSvTu3RtPnjzB0aNHceDAAdy5cweDBg0CANSrVw8tW7ZUB9YqGzduxJBna4ErlUp4eHjgjz/+wLVr1zB9+nR88cUX2Lp1a2l+REhPT8f//vc//PLLL7h69SqqVauGlJQUjBgxAidOnMC///6LunXrokePHkh5tmTk2bNnAQBr1qxBXFyc+vnx48cxfPhwTJo0CdeuXcPKlSuxdu1adYC8bds2LFy4ECtXrsStW7ewY8cONGnSpFT1ZeVEsBcWHR0tAIjo6OgKOf+UA1MEpNkCECImpkIuwRgzUBkZGeLatWsiIyNDszM1VQgK2Sp3S00tcb07deokJk2aJJYtWyZsbGxESEiI+tiGDRtE/fr1hVKpVO/LysoSZmZmYt++fUIIIbZv3y7yv4UVti+/nTt3ChsbG5GbmyvCwsKEi4uLmDRpkvjss8+EEEKMHj1aDBkypMjXz5gxQ5iYmIiHDx+q9x0/flxYW1uLzMxMrbK1a9cWK1euVL/O3NxcJCcnq48HBAQILy8voVAo1Pvq168v5s6dK4QQYv/+/cLIyEjcu3dPffzq1asCgDhz5owQQoiFCxeK2rVrq4/fuHFDABDh4eFF3sOHH34o3nrrLfXzESNGiN69exdZfs2aNQKACAsLK7KMEEIoFAphZWUldu3apd4HQGzfvl2rXJcuXcScOXO09m3YsEG4uroKIYSYP3++qFevnsjOzi72eiVV6N/IMxX9/m3ouCXRAGRkZQFKEwDcksgYqzr+/PNPfPTRRzhw4AA6deqk3n/p0iXcvn0bVlZWsLS0hKWlJezt7ZGZmaked1hWHTt2REpKCi5evIijR4+iU6dO8PPzU7cuHj16FH5+fsWeo0aNGnByctKqb2pqKhwcHNT1tbS0RGRkpFZ9vby8YGVlpX7u7OwMb29vrbF9zs7OePjwIQCanOPp6QlPT0/1cW9vb9ja2iI8PBwA8Pbbb+Pu3bv4999/AVArYvPmzdGgQQP1a5YvX44WLVrAyckJlpaW+Pnnn3Hv3r1Sfd9kMhmaNm2qtS8+Ph5jxoxB3bp1YWNjA2tra6Smpj733JcuXcI333yj9b0aM2YM4uLikJ6ejgEDBiAjIwO1atXCmDFjsH37dnVXNKtcPAvCADxruQfAYxIZYyVgbg48G2P3woSgf0KPHlGSbnt7Stytkp2t+cdkbl6qUzdr1gwXLlzA6tWr0bJlS/U4wtTUVLRo0aJANyoAreCsLGxtbeHj44OQkBCEhobi9ddfx6uvvopBgwbh5s2buHXrllbAWhgLCwut56mpqXB1dVUHmvmvp2JiYqJ1TCKRFLpPWYp8gC4uLnjttdfw+++/o23btvj9998xduxY9fHNmzfjk08+wfz589GuXTtYWVlh3rx56i7tkjIzMyswznPEiBF4/PgxFi9ejBo1akAul6Ndu3bIzs4u9lypqamYOXMm+vXrV+CYqakpPD09cePGDRw8eBAHDhzAuHHjMG/ePBw9erTA94tVLA4SDcCtUw3Vj7klkTH2XBIJpbspL5aWlNA4O5uCRtU/ovR0ICKCknM7OZU6SKxduzbmz58PPz8/GBkZYdmyZQCA5s2bY8uWLahWrRqsra3L7z6e6dSpE44cOYIzZ85g9uzZsLe3R8OGDTF79my4urqiXr16pTpf8+bN8eDBAxgbG2tNznhRDRs2RHR0NKKjo9WtideuXUNiYiK8vb3V5YYOHYopU6Zg8ODBuHPnDt5++231sZMnT6J9+/YYN26cet+LtsbmPfePP/6IHj16AKDJRgkJCVplTExMoFAotPY1b94cN27cKHbcp5mZGXr27ImePXviww8/RIMGDXDlyhU0b968XOrOSoa7mw1AZqbmx8QfohhjOiOTaX9SVXVzJCdTsHj5MuVefE5LUl716tXDkSNHsG3bNnVy7aFDh8LR0RG9e/fG8ePHERkZiZCQEEycOBExMTEvfBt+fn7Yt28fjI2N1d2yfn5+2Lhx43NbEQvj7++Pdu3aoU+fPti/fz/u3r2LU6dO4csvv8S5c+fKXE9/f380adIEQ4cOxYULF3DmzBkMHz4cnTp1QsuWLdXl+vXrh5SUFIwdOxadO3eGm5ub+ljdunVx7tw57Nu3Dzdv3sRXX32lnkDyourWrYsNGzYgPDwcp0+fxtChQ2FmZqZVxsvLC4cOHcKDBw/w9OlTAMD06dOxfv16zJw5E1evXkV4eDg2b96MadOmAaDZ1L/++iv+++8/3LlzB7/99hvMzMxQo0aNcqk3KzkOEg1AljpIVILTUjHG9IazM9C4MS2ZZmxMqXPu36dg8fbtEq/oUr9+fRw+fBibNm3Cxx9/DHNzcxw7dgzVq1dHv3790LBhQ7z77rvIzMwsl5bFjh07QqlUagWEfn5+UCgUzx2PWBiJRII9e/bg1VdfRWBgIOrVq4e3334bUVFRcH6BdbMlEgn+/vtv2NnZ4dVXX4W/vz9q1aqFLVu2aJWzsrJCz549cenSJQwdOlTr2Pvvv49+/fph0KBBaNOmDR4/fqzVqvgifv31Vzx9+hTNmzfHsGHDMHHiRFSrVk2rzPz583HgwAF4enqiWbNmAICAgADs3r0b+/fvR6tWrdC2bVssXLhQHQTa2tpi1apV6NChA5o2bYqDBw9i165dcMi7xCSrFBIhKjERVhUVExMDT09PREdHw8PDo9zP32XMIRz+pQsgzYVQ8AgBxphGZmYmIiMjtXL26YRSSUm5Hz2i8ZAmJrQsoOqTrRAGtaILqzqK+xup6PdvQ6d/7VLLl9OgaFNToE0b4MyZosvm5ADffAPUrk3lfXyA4GDtMgoF8NVXtJKAmRmVnTVLO5+XEMD06TTmxswM8Pen9U31hI2UPolKJBzPM8b0lFRKq7Y0aAA0agTUqKEdIF69Cty5QwEkt00wZhD0K0jcsgUICgJmzKBErj4+QEAA8CwdQAHTpgErVwJLlwLXrgEffAD07QtcvKgp87//AT/9BCxbBoSH0/Pvv6fXqHz/PbBkCbBiBXD6NA34DggAMjMr9n5LKDODPn1LpCWf8cYYYzpjZkZrQqukpND/0ydPgOvX6f/1w4f0IZ4xprf0K0hcsAAYMwYIDKTlolasoNlyq1cXXn7DBuCLL4AePYBatYCxY+nx/PmaMqdOAb17A2+8QS2U/fsDXbtqWiiFABYtooCzd2+gaVNg/XoaV7NjRwXfcMkkpqUD4CCRMWagrK1pGUBHR2pdzMigVV0uXQKiovTmAzljTJv+BInZ2bQElL+/Zp9USs9DQwt/TVYWdTPnZWYGPFteCQDQvj1w6BBw8yY9v3SJjnfvTs8jI4EHD7Sva2NDXd3FXTc5WbPlTWRYAaIEJUmFLL1Cr8MYYxXGwoI+qDdtCnh60v9upZLGMHKiZMb0kv7MgkhIoK6H/DPBnJ2pe6IwAQHU+vjqqzTW8NAh4K+/tLswPv+cArkGDQAjIzo2ezagmgH24IHmOvmvqzqW39y5wMyZpb/HMjKtfhkAYGL1FADP7mKMGTBjY/r/Wq0ajU9MStLO6RgTQz08Tk4FGwEYY5VKf1oSy2LxYqBuXQoAZTJg/Hjqqs6bJ2brVmDjRuD332mc47p1wA8/0NeymjqV/rGptmvXXvxeipGTTfdjZMyfthljVYREAlhZAR4emlnPCgWNVYyPB/77D7hxg8YxlmIFEsZY+dGfINHRkVr64uO198fHUw6uwjg50bjBtDQa13L9Oq0MUKuWpsynn1Jr4ttvUzqGYcOAjz6i1kBAc+7SXFcupzE2qi3PWpwVISeLMmhLTXiQN2OsCpNKqVfIxoaep6TQjOgrV4DYWBrqw1gV0LdvX9jZ2aF///7qfYmJiWjZsiV8fX3RuHFjrFq1Soc1JPoTJMpkQIsW1GWsolTS83btin+tqSng7k7jWrZtowkoKunpKJCB2shI88m0Zk0KBvNeNzmZZjk/77qVJPEU/RLlJDrquCaMMVaBJBIKEOvWpQ/1rq6UbzEnB4iLo2FJjFUBkyZNwvr167X2WVlZ4dixYwgLC8Pp06cxZ84cPH78WEc1JPozJhGg9DcjRgAtWwKtW9Os47Q06kIGgOHDKRhUtQKePk2fLn196evXX1PwN2WK5pw9e9IYxOrVKXfXxYs0jnHUKDoukQCTJwPffkv/mGrWpLyKbm5Anz6VduvFEbn0Y5IacUsiY+wlIZfT/3tXVyAxkQJExzwflJOT6f3B0ZHXK2UGx8/PDyEhIVr7jIyMYP5s/fOsrCwIIaDr9U70pyURAAYNovGC06dT4BcWRsmxVZNK7t2jT5MqmZmUusbbm/IjurvTzOW8+bmWLqW0N+PGUQqGTz4B3n+fEmqrTJkCTJgAvPce0KoVDaYODtabQdPKXPoHaGTM43IYYy8ZqRSwtwfq1dNeNzo+nhoHLl+mdaNTUjhJdxl5eXlh0aJFuq6GwTh27Bh69uwJNzc3SCQS7CgkXd7y5cvh5eUFU1NTtGnTBmeKWxgkj8TERPj4+MDDwwOffvopHB1124OoX0EiQJNPoqJo7Mnp05SKRiUkBFi7VvO8UyeaNJKZSZ8y16+nFsC8rKyoRTIqinJzRURQq6FMpikjkdDKLQ8e0LkOHqR/SHpCLqExjyay5xRkjDEDoFAo0L59e/Tr109rf1JSEjw9PfHll18+/yT29jQrWghaDvDGDVrVJT6eU+roiZCQEEgkEiQmJuq6KuUqLS0NPj4+WL58eaHHt2zZgqCgIMyYMQMXLlyAj48PAgIC8LCohUHysLW1xaVLlxAZGYnff/8d8fnnS1Qy/QsSWQEyWAIATGVGOq4JY4y9OCMjI6xduxbBwcHYuHGjev+ECRNgb2+PGTNmPP8kDg7UO+TtTZMYpVL6kB8dDdy+XYG115adnV1p19IXVfGeU1JSkJycrN6yipkk1b17d3z77bfo27dvoccXLFiAMWPGIDAwEN7e3lixYgXMzc2xuqiFQQrh7OwMHx8fHD9+vNT3Up44SDQAilz6MRmbcHczY6xqqFevHr777jtMmDABcXFx+Pvvv7F582asX78esmc9PVlZWfjkk0/g7u4OCwsLtGnTpsA4Lpib0zrRPj74ZPVqvPnxxxRAAli0aBEkEgmC//pLXbxOnTr45ZdfAABKpRLffPMNPDw8IJfL4evri+Dg4GLr7efnh/Hjx2Py5MlwdHREQEAAAOC///5D9+7dYWlpCWdnZwwbNgwJeSba+Pn5YcKECZg8eTLs7Ozg7OyMVatWIS0tDYGBgbCyskKdOnWwd+9eresdPXoUrVu3hlwuh6urKz7//HPkPmsp/fnnn+Hm5gZlvhRBvXv3xqhn4+4jIiLQu3dvODs7w9LSEq1atcLBgwdL8iNSGzlyJPr06YPZs2fDzc0N9evXBwBs2LABLVu2hJWVFVxcXDBkyBB1a9ndu3fRuXNnAICdnR0kEglGjhyp/r7PnTsXNWvWhJmZGXx8fPDnn3+qr/f06VMMHToUTk5OMDMzQ926dbFmzZpS1bm0vL29YWNjo97mquY+lFJ2djbOnz8P/zwLdEilUvj7+yO0qAU6nomPj0fKs8U5kpKScOzYMfX3WmcEe2HR0dECgIiOjq6Q85u7RAtAiFq+9yrk/Iwxw5WRkSGuXbsmMjIyChxLzUotcsvIyShx2fTs9OeWLQulUin8/PxEly5dRLVq1cSsWbO0jo8ePVq0b99eHDt2TNy+fVvMmzdPyOVycfPmzULPt3PnTmFjYyNyc3KEEEL06dNHONrbi8+GDxfi5k0Rc/u2ACBu3bolhBBiwYIFwtraWmzatElcv35dTJkyRZiYmBR5fiGE6NSpk7C0tBSffvqpuH79urh+/bp4+vSpcHJyElOnThXh4eHiwoUL4vXXXxedO3fWep2VlZWYNWuWuHnzppg1a5YwMjIS3bt3Fz///LO4efOmGDt2rHBwcBBpaWlCCCFiYmKEubm5GDdunAgPDxfbt28Xjo6OYsaMGUIIIZ48eSJkMpk4ePCg+jqPHz/W2hcWFiZWrFghrly5Im7evCmmTZsmTE1NRVRUlPo1NWrUEAsXLizynkeMGCEsLS3FsGHDxH///Sf+++8/IYQQv/76q9izZ4+IiIgQoaGhol27dqJ79+5CCCFyc3PFtm3bBABx48YNERcXJxITE4UQQnz77beiQYMGIjg4WERERIg1a9YIuVwuQkJChBBCfPjhh8LX11ecPXtWREZGigMHDoidO3cWWb/nKe5vRPX+fe3aNZGUlKTeMjMzS3RuAGL79u3q57GxsQKAOHXqlFa5Tz/9VLRu3Vr9vEuXLsLR0VGYmZkJd3d3cerUKXH69Gnh4+MjmjZtKpo0aSJWrFhRthsuRxwkloOKDhIlXkcFIETd9v9VyPkZY4aruDdAfI0itx4be2iVNZ9tXmTZTms6aZV1/N6xQJmyCg8PFwBEkyZNRM6z4E4IIaKiooSRkZGIjY3VKt+lSxcxderUQs/19OlTIZVKxdmzZ4VSqRT29vZi7tSpok3jxkKcPSt+mzVLuLu4CJGbK4QQws3NTcyePVvrHK1atRLjxo0rsr6dOnUSzZo109o3a9Ys0bVrV619qveFGzduqF/3yiuvqI/n5uYKCwsLMWzYMPW+uLg4AUCEhoYKIYT44osvRP369YVSqVSXWb58ubC0tBQKhUIIIUTv3r3FqFGj1MdXrlwp3Nzc1McL06hRI7F06VL185IEic7OziIrK6vIMkIIcfbsWQFApKSkCCGEOHLkiAAgnj59qi6TmZkpzM3NCwRR7777rhg8eLAQQoiePXuKwMDAYq9VGiUJEsv6/l3WINFQcHezAZB6nAUAOHok6bgmjDFWvlavXg1zc3NERkYiJiZGvf/KlStQKBSoV68eLC0t1dvRo0cRERFR6LlsbW3h4+ODkJAQXLlyBTKZDO998gku3ryJVCMjHD1/Hp18fICrV5F89y7u37+PDh06aJ2jQ4cOCA8PL7bOLVq00Hp+6dIlHDlyRKueDRo0AACtujZt2lT92MjICA4ODmjSpIl6n/OzTB6qLtvw8HC0a9cOEtWKNM/ql5qaqv5eDf1/e3ceF1XV/wH8c2dg2AYEBNk3FRAUEDF5qSnkBlo+LmlqJOhjmgSaKW6l6E99Qn3UNLPslSVWKJZbPm6Z6IyC5oLiBikQm4QimsuwM3N+f1y5Mg4IKMqA3/frdV/O3HvunXPmzjhfzhoSgp07dwp96OLi4jB27FiIHs0PrFAoEBUVBQ8PD5iamkIqlSItLQ25ublPLeOTvLy8hG4A1ZKTkzF06FA4OjrC2NgYAQEBAPDUa2dkZKCkpAQDBw5Ue79++OEH4b0KDw9HfHw8unbtijlz5uDkyZONymtzsrCwgFgs1hhwcuvWLVjXtUCHFtOueRJJrdijKXD09bh6UhJCyGOK+Yo6j4lF6gPhCqPqHnkp4tTrE7I/yn6ufFU7efIkPv/8cxw+fBjLli3DpEmTcOTIEXAcB4VCAbFYjOTkZIjF6nmVSqV1XrN6/jk9PT0EBATA3NwcHh4eSLx5E/IrVzDr3XeBigog+9nLYFRzrWnwgdjQoUOxYsUKjbQ2NjbCY90n5nPkOE5tX3Uw+GQfw6cZOnQoGGPYv38/XnvtNZw4cQKff/65cDwqKgq///47Vq1ahY4dO8LAwACjRo1q9OCTJ8tcXFyMoKAgBAUFIS4uDpaWlsjNzUVQUNBTr61Q8J/J/fv3w87OTu2Y3qMpjgYPHoycnBwcOHAAv//+O/r374+IiAisWrWqUXluDhKJBH5+fkhISMDwR3Mtq1QqJCQkIDIysnkz9wwoSGwBVFX8X2/6elTxSwhpOCOJUf2JXnDaupSUlGDChAkIDw/HG2+8ARcXF3h5eWHjxo0IDw+Hr68vlEolCgsL0adPnwZfNyAgAN9//z10dHQQHBwMgA8ct8XH43pGBgLHjQNMTGBy8yZsbWyQlJQk1IABQFJSEnr06NGosnTr1g07d+6Es7MzdHSa7mfVw8MDO3fuBGNMCCCTkpJgbGwMe3t7AIC+vj5GjhyJuLg4ZGRkwN3dHd26dVMrz4QJE4SRuAqFAtnPESBX+/PPP3Hnzh0sX74cDg4OAIBz586ppamueVQqHy8E4enpCT09PeTm5qq970+ytLREWFgYwsLC0KdPH8yePVtrgkSFQoGMGiPos7KykJKSAnNzczg6OmLmzJkICwtD9+7d0aNHD6xdu1YYoNTSUJDYElwZDQB4UNimmTNCCCFNY/78+WCMYfny5QD4CZ1XrVqFqKgoDB48GG5ubggJCUFoaChWr14NX19f3L59GwkJCfD29sabb75Z63X79u2Lhw8fYt++fcK1AwMDMWrUKNjY2MDtUTMwrKwwe84cLFq0CB06dEBXGxts3rEDKSkpatPyNERERAS+/fZbjBs3DnPmzIG5uTkyMjIQHx+PTZs2adSENtSHH36ItWvXYtq0aYiMjMS1a9ewaNEizJw5U2hOBvgm57feegtXr17Fe++9p3YNV1dX7Nq1C0OHDgXHcVi4cGGjairr4ujoCIlEgvXr12Pq1Km4cuUKltZcpAKAk5MTOI7Dvn37MGTIEBgYGMDY2BhRUVH4+OOPoVKp8Prrr+P+/ftISkqCiYkJwsLCEB0dDT8/P3Tu3Bnl5eXYt28fPDw8njvPTeXcuXPCyG0AmDlzJgAgLCwMsbGxGDNmDG7fvo3o6GjcvHlTGDVf3Z2gRWnmPpGtwoseuAK9fxjA2IB3rr2Q6xNCWq6ndcrXVjKZjInFYnbixAmNY4MGDWL9+vVjKpWKVVRUsOjoaObs7Mx0dXWZjY0NGzFiBLt06dJTr+/j48Osra2F53fu3GEcx7GxY8eqpVMqlWzx4sXMztaW6eroMB9XV3bwm28YU9Q9WjsgIIB99NFHGvuvX7/ORowYwUxNTZmBgQHr1KkTmzFjhjDopLbzahswgicGQshkMvbaa68xiUTCrK2t2dy5c9UG+FSXw8bGhgFgmZmZaseysrLYG2+8wQwMDJiDgwP78ssvNfLSkIErw4YN09i/detW5uzszPT09FjPnj3Z3r17GQB24cIFIc2SJUuYtbU14ziOhYWFMcb4Ue1r165l7u7uTFdXl1laWrKgoCAml8sZY/xAIA8PD2ZgYMDMzc3ZsGHD2F9//VVn/urzIgeutHYcY7SO0fO6ceMGHBwckJeXJzQBNBWlSgkd/RKg0hhDw9KxN9a1Sa9PCGnZysrKkJWVBRcXF+hryVKiLY5KBRQWAn//zT8G+DWh7exoXehW4GnfkRf5+90aUCc3LcfAAMb3CjA2pN4BhBDS5EQiwNoa6NJFmIgbRUXAlSt88Eh1KeQVRUGiltMR6QCPZiqSGlGQSAghL4xEAri4AJ068Su5KJVAfj6tBU1eWRR1tARCkEgxPSGEvHBSKb8u9O3bAMepNzlXVQFNOIKZEG1Gn3QtV6WqAhg/9YGhAQWJhBDyUnAc0K6d+r7794HMTMDGBrCy4pupCWnF6BOu5QoeFgB6/ILfZm3odhFCSLO5c4cf2JKfD6Sm8kEjIa0YRR1arkJZAbThlziysqYVVwghpNm4uADOznzzc1kZkJ4OZGQAj5bEI6S1oeZmLVehrACU/Kz1UgOaioEQQpoNx/FT45iZ8dPlFBYC9+7xNYoODprN04S0cFSTqOVKKkuAKn49S0N9iukJIaTZicV8UOjpCRgb81PkPFqCjpDWhKIOLVd4pxy45wwA0NOl20UIIVrDwABwcwMUCn5EdLV79wA9Pf44IS0Y1SRquQelZai+TSZSam4mhBCtwnF8bSL3qM94ZSWQlcUPbMnL4+dabEaxsbEwNTVt1jyQlouCRC2nKH3cIdrI4NkWiSeEEG2iVCrRq1cvjBw5Um3//fv34eDggE8//bSZctYEGHvcBH3rFr9qS1FRq1y1JTAwEDNmzGjubJAXiIJELWckNhMe6+nR6GZCSMsnFosRGxuLQ4cOIS4uTtg/bdo0mJubY9GiRc2Yu8cYY6hq7GorEgnQsSPg6so3OVdWAtnZwLVrQHHxC8lnU6usrGzuLBAtQUGiljPTsRYe6+k1Y0YIIS0GYwzFFcUvfWONqC1zc3PD8uXLMW3aNBQUFODXX39FfHw8fvjhB0geDQIpLy9HVFQU7OzsYGRkBH9/f8hksjqvGRUVhbfeekt4vnbtWnAch0OHDgn7OnbsiE2bNtV6vkwmA8dxOHjwIPz8/KCnp4fExESoVCrExMTAxcUFBgYG8PHxwY4dOzTO++233+Dr6wsDAwP0GzEChZaWOHjtGjxGj4ZJ9+549+23UXLvnnBeeXk5pk+fjnbt2kFfXx+vv/46zp49CwBQqVSwt7fH119/rZbHCxcuQCQSIScnBwCwZs0aeHl5wcjICA4ODvjwww+hUCgadhMAZGdng+M4bN++HQEBAdDX10dcXBzu3LmDcePGwc7ODoaGhvDy8sK2bduE8yZMmAC5XI5169aB4zhwHIfs7GwAwJUrVzB48GBIpVJYWVlh/PjxKCoqEs7dsWMHvLy8YGBggLZt22LAgAEobiEB9KuGRkJoOUXJ479iafAcIaQhSipLII2R1p+wiSnmK2AkMWpw+mnTpmH37t0YP348Ll++jOjoaPj4+AjHIyMjkZqaivj4eNja2mL37t0IDg7G5cuX4erqqnG9gIAAbNq0CUqlEmKxGHK5HBYWFpDJZAgODkZ+fj4yMzMRGBj41HzNmzcPq1atQvv27WFmZoaYmBj89NNP2LhxI1xdXXH8+HG89957sLS0REBAgHDe4sWL8eWXX8LQ0BDvvPMO3hk7Fnp6eti6fTsUWVkYER6O9d98g7lz5wIA5syejZ07d2LLli1wcnLCypUrERQUhIyMDJibm2PcuHHYunUrwsPDhdeIi4tD79694eTkBAAQiUT44osv4OLigr/++gsffvgh5syZg6+++qrB96G6zKtXr4avry/09fVRVlYGPz8/zJ07FyYmJti/fz/Gjx+PDh06oEePHli3bh2uX7+OLl26YMmSJQAAS0tL3Lt3D/369cP777+Pzz//HKWlpZg7dy7eeecdHD16FAUFBRg3bhxWrlyJESNG4OHDhzhx4kSj/sAgLxEjzy0vL48BYHl5eU1+7W92X2J8ZxbGioub/PKEkBautLSUpaamstLSUmGfolzBsBgvfVOUKxqd/7S0NAaAeXl5scrKSmF/Tk4OE4vFLD8/Xy19//792fz582u91j///MNEIhE7e/YsU6lUzNzcnMXExDB/f3/GGGM//fQTs7OzqzMvx44dYwDYnj17hH1lZWXM0NCQnTx5Ui3tpEmT2Lhx49TOO3LkiHA8JiaGAWCZmZnCvg+mTGFBQUGMMcYUt28zXR0dFvfdd8LxiooKZmtry1auXMkYY+zChQuM4ziWk5PDGGNMqVQyOzs79vXXX9dZhl9++YW1bdtWeL5582bWpk2bOtNnZWUxAGzt2rV1pqn25ptvslmzZgnPAwIC2EcffaSWZunSpWzQoEFq+6p/I69du8aSk5MZAJadnV3v6zWV2r4jT+btRfx+twZUk6jlzv19BoAXAKpJJIQ0jKGuIRTzG97k2JSv21jff/89DA0NkZWVhRs3bsDZ2RkAcPnyZSiVSri5uamlLy8vR9u2bWu9lqmpKXx8fCCTySCRSCCRSDBlyhQsWrQICoUCcrlcreavLt27dxceZ2RkoKSkBAMHDlRLU1FRAV9fX7V93t7ewmMrKysYGhqiffv2j/dZW+PMo+bkzNOnUVlVhd6WlvxoaHt76OrqokePHkhLSwMAdO3aFR4eHti6dSvmzZsHuVyOwsJCjB49WrjmkSNHEBMTgz///BMPHjxAVVUVysrKUFJSAkPDht+PmmUG+MFFn332GX7++Wfk5+ejoqIC5eXl9V7z4sWLOHbsGKRSzZrszMxMDBo0CP3794eXlxeCgoIwaNAgjBo1CmZmZrVcjTQ3ChK1nL7lLf4Bp4SODo1uJoTUj+O4RjX7NpeTJ0/i888/x+HDh7Fs2TJMmjQJR44cAcdxUCgUEIvFSE5Ohlis/n9fbQFItcDAQMhkMujp6SEgIADm5ubw8PBAYmIi5HI5Zs2aVW++jIwev3fV/fv2798POzs7tXR6T3QU19V9PE0Zx3Fqz6v3qVQq/on14/7muHMH+OcfwNZWYxR0SEiIECRu3boVwcHBQpCcnZ2Nt956C+Hh4fjPf/4Dc3NzJCYmYtKkSaioqGhUkFizzADw3//+F+vWrcPatWuFPo8zZsxARUXFU6+jUCgwdOhQrFixQuOYjY0NxGIxfv/9d5w8eRKHDx/G+vXr8emnn+L06dNwcXFpcH7Jy0FBopYrKX00x5a4AgBNzEoIaR1KSkowYcIEhIeH44033oCLiwu8vLywceNGhIeHw9fXF0qlEoWFhejTp0+DrxsQEIDvv/8eOjo6CA4OBsAHjtu2bcP169fr7Y/4JE9PT+jp6SE3N7dBtZAN1aFTJ0gkEiQVFcGpY0eguBiV2dk4e+oUZkybJqR79913sWDBAiQnJ2PHjh3YuHGjcCw5ORkqlQqrV6+GSMSPQ/3555+bJH9JSUkYNmwY3nvvPQD8QJrr16/D09NTSCORSKB8Yh7Ibt26YefOnXB2doaOTu0hBsdx6N27N3r37o3o6Gg4OTlh9+7dmDlzZpPknTQdGt2s5UrLHn0BdZ7+1xshhLQk8+fPB2MMy5cvBwA4Oztj1apVmDNnDrKzs+Hm5oaQkBCEhoZi165dyMrKwpkzZxATE4P9+/fXed2+ffvi4cOH2LdvnxAQBgYGIi4uDjY2NhrN1/UxNjZGVFQUPv74Y2zZsgWZmZk4f/481q9fjy1btjxz+Y2MjBAeHo7ZCxbgUHY2UsvKMPmzz1BSVoZJo0YJ6ZydndGrVy9MmjQJSqUS//rXv4RjHTt2RGVlJdavX4+//voLP/74o1oQ+TxcXV2FGr+0tDR88MEHuHXrlloaZ2dnnD59GtnZ2SgqKoJKpUJERATu3r2LcePG4ezZs8jMzMRvv/2GiRMnQqlU4vTp0/jss89w7tw55ObmYteuXbh9+zY8PDyaJN+kaVGQqOWyUx71Z6nUb96MEEJIE5HL5diwYQM2b96s1iT6wQcfCAERYwybN29GaGgoZs2aBXd3dwwfPhxnz56Fo6Njndc2MzODl5cXLC0t0alTJwB84KhSqZ65JnDp0qVYuHAhYmJi4OHhgeDgYOzfv/+5m0eXL1+Ot99+G+NDQ9FtwABk3LmD37Ztg1nNQLasDCHjxuHixYsYMWIEDGos9efj44M1a9ZgxYoV6NKlC+Li4hATE/Nceaq2YMECdOvWDUFBQQgMDIS1tTWGDx+uliYqKgpisRienp6wtLREbm4ubG1tkZSUBKVSiUGDBsHLywszZsyAqakpRCIRTExMcPz4cQwZMgRubm5YsGABVq9ejcGDBzdJvknT4hijcefP68aNG3BwcEBeXh7s7e2b9NrdQrfiwo/vAuJysCqaKJEQoq6srAxZWVlwcXGBvj79MdmqqFT88n6MAQ4OQJs2j5f/Iw32tO/Ii/z9bg2oJlHLlZc96rDNqZo3I4QQQl6u8nJ+7efyciAjA0hPB8rKmjtX5BVCQaKWM4ApAEAkogpfQgh5pRgYAF268COhOQ548AC4ehW4cYMPHgl5wShI1HJtxPw0CSKa/YYQQl49YjFgbw907sw3NzMG3LwJXLkC1DMdDSHPi6bA0XJl5Xz/E5GImpsJIeSVpa8PdOwI3L8P5OXxqys8MQ8jIU2NgkQtV1rGB4ecmIJEQgh5pXEcYGoKmJgAVVWPB7FUVQEFBYCNDVDH3ISEPAtqbtZy+aUZAACm8/KX2CKEEKKFRCL1dVr//hu4dYtvgr59W2PVFkKeFQWJWs7A8Qr/r21282aEEEKIdjI15Zujq6qAnBwgLQ1QUMUCeX4UJGq5yiq+OUGsSyPZCCGE1MLEBPD05OdSFImAkhLgzz+B7GygsrK5c0daMAoStVxVBT+sWUe3qplzQgghRGuJRICVFeDlBbRty+8rKuKbogl5RhQkarmHV18HAJTdopngCSGE1ENXF3BxATp1AoyNAVvbx8dUjwdALl68GF27dn35+SMtivYFiRs2AM7OfP8Kf3/gzJm601ZWAkuWAB068Ol9fIBDh9TTODvzI8Ce3CIiHqcJDNQ8PnXqCyhc4ylLjB89ookSCSGtx4QJEzTWAt6xYwf09fWxevXq5slUayKVAu7u6tPkZGby2wueX9HZ2Rlr1659oa9BXg7tGiu/fTswcyawcSMfIK5dCwQFAdeuAe3aaaZfsAD46Sfg22/5v5p++w0YMQI4eRLw9eXTnD2rPjP9lSvAwIHA6NHq15o8mQ84q9VYdL45MSV/i8Q6NAUOIaT12rRpEyIiIrBx40ZMnDix1jQVFRWQ1BzV20yUSiU4joNIpH31LHUqLeXnWAT4fx8+bNTpjDEolUro0BQ7rxTt+oSvWcMHaxMn8p1wN27kg7Xvv689/Y8/Ap98AgwZArRvD4SH849r/hVqackvaVS97dvH1zwGBKhfy9BQPZ2JyYsrZyOIVfxi5HoSWtSdENI4xcV1b08uAfy0tKWl9ad9HitXrsS0adMQHx+vFiAGBgYiMjISM2bMgIWFBYKCgjTOvXLlCkQiEW7fvg0AuHv3LkQiEcaOHSukWbZsGV5//XXhuVwuR48ePaCnpwcbGxvMmzcPVVV19/uOjY2Fqakp9u7dC09PT+jp6SE3Nxfl5eWIioqCnZ0djIyM4O/vD5lMpnHevn374O7uDkNDQ4waNQolJSXYsmULnJ2dYWZmhunTp0NZozLjn3/+QWhoKMzMzGBoaIjBgwcjPT0dAPDgwQMYGBjg4MGDanncvXs3jI2NUVJSAgCYO3cu3NzcYGhoiPbt22PhZ5+h0tWVr2FUqfggsazsceD4BJlMBo7jcPDgQfj5+UFPTw+JiYnIzMzEsGHDYGVlBalUitdeew1HjhxRu2c5OTn4+OOPwXEcOO7xb1diYiL69OkDAwMDODg4YPr06Siu8eH56quv4OrqCn19fVhZWWHUqFF13pOWbsSIETAzM9MoY137m4v2BIkVFUByMjBgwON9IhH//NSp2s8pL+ebmWsyMAASE+t+jZ9+Av7978eTkFaLiwMsLPh1MufP50eH1aW8nF9Ds3pr5F9kjaHD8TWaJob69aQkhBB1Umnd29tvq6dt167utIMHq6d1dtZM86zmzp2LpUuXYt++fRgxYoTG8S1btkAikSApKQkbN27UON65c2e0bdsWcrkcAHDixAm15wAfFAYGBgIA8vPzMWTIELz22mu4ePEivv76a3z33XdYtmzZU/NZUlKCFStWYNOmTbh69SratWuHyMhInDp1CvHx8bh06RJGjx6N4OBgIaCrPu+LL75AfHw8Dh06BJlMhhEjRuDAgQM4cOAAfvzxR3zzzTfYsWOHcM6ECRNw7tw57N27F6dOnQJjDEOGDEFlZSVMTEzw1ltvYevWrWr5i4uLw/Dhw2H4qBXM2NgYsbGxSE1Nxbp16/Dtt9/i82++4ZugXVz431eVCkhP57c6RkHPmzcPy5cvR1paGry9vaFQKDBkyBAkJCTgwoULCA4OxtChQ5GbmwsA2LVrF+zt7bFkyRIUFBSgoKAAAJCZmYng4GC8/fbbuHTpErZv347ExERERkYCAM6dO4fp06djyZIluHbtGg4dOoS+ffs+9Z60ZB999BF++OGHBu9vNkxb5OczBjB28qT6/tmzGevRo/Zzxo1jzNOTsevXGVMqGTt8mDEDA8YkktrTb9/OmFjMv1ZN33zD2KFDjF26xNhPPzFmZ8fYiBF153XRIj6vj7Y8gAFgeXl5DS5uQxla3WAAY517Nv21CSEtX2lpKUtNTWWlpaUax2r8N6WxDRmintbQsO60AQHqaS0sNNM0VlhYGJNIJAwAS0hIqDVNQEAA8/X1rfdaI0eOZBEREYwxxmbMmMFmz57NzMzMWFpaGquoqGCGhobs8OHDjDHGPvnkE+bu7s5UKpVw/oYNG5hUKmVKpbLW62/evJkBYCkpKcK+nJwcJhaLWf4Tvyf9+/dn8+fPVzsvIyNDOP7BBx8wQ0ND9vDhQ2FfUFAQ++CDDxhjjF2/fp0BYElJScLxoqIiZmBgwH7++WfGGGO7d+9mUqmUFRcXM8YYu3//PtPX12cHDx6s8z3673//y/z8/ITnixYuZD6enoydO8f/9j1R9mPHjjEAbM+ePXVes1rnzp3Z+vXrhedOTk7s888/V0szadIkNmXKFLV9J06cYCKRiJWWlrKdO3cyExMT9uDBg3pfr7Ge9h3Jy8t7Yb/f9Tl27Bh7++23G7y/OWhPTeKzWLcOcHXl+yNKJEBkJN9UXVc/ke++4/8krjnaCwCmTOH7Pnp5ASEhwA8/ALt38x18azN/Pl9FX72lpjZtuWpQKfmyaEE3HEJIC6NQ1L3t3KmetrCw7rRPtGwiO1szzbPw9vaGs7MzFi1aBEUdF/Hz86v3OgEBAUIzr1wuR79+/dC3b1/IZDKcPXsWlZWV6N27NwAgLS0NPXv2VGsG7d27NxQKBW7cuFHna0gkEnh7ewvPL1++DKVSCTc3N0ilUmGTy+XIrPHbYWhoiA4dOgjPrays4OzsDGmN6lcrKysUFhYK+dPR0YG/v79wvG3btnB3d0daWhoAYMiQIdDV1cXevXsBADt37oSJiQkG1GiJ2759O3r37g1ra2tIpVIsWLBAqO0DwP9O6uryXbuqaxYBPua/d09YtaV79+5q74NCoUBUVBQ8PDxgamoKqVSKtLQ09WvX4uLFi4iNjVV7r4KCgqBSqZCVlYWBAwfCyckJ7du3x/jx4xEXFyc0nWub48ePY+jQobC1tQXHcdizZ49Gmg0bNsDZ2Rn6+vrw9/fHmacNwtVi2hMkWlgAYjG/tFBNt27xfQRrY2kJ7NnDd4jJyeEnD5VK+f6JT8rJAY4cAd5/v/68VH85MzJqP66nx/dZrN6MjWtP1wTK8QAAUKlT9MJegxDSOhkZ1b092VPnaWkNDOpP+yzs7Owgk8mQn5+P4OBgPKyl645RAy4eGBiI1NRUpKenIzU1Fa+//joCAwMhk8kgl8vRvXt3oRn2WRkYGKgFlgqFAmKxGMnJyUhJSRG2tLQ0rFu3TkinW3N0MQCO42rdp1I1fHCiRCLBqFGjhCbnrVu3YsyYMcKgklOnTiEkJARDhgzBvn37cOHCBXz66aeoqG1Us4GBen+B27f53768PACa739UVBR2796Nzz77DCdOnEBKSgq8vLxqv3YNCoUCH3zwgdp7dfHiRaSnp6NDhw4wNjbG+fPnsW3bNtjY2CA6Oho+Pj64d+9eg9+X5/Hw4UM8ePBA2MrLy+tMW1xcDB8fH2zYsKHW49u3b8fMmTOxaNEinD9/Hj4+PggKChL+EGhJtCdIlEgAPz8gIeHxPpWKf96z59PP1dcH7Oz4JYl27gSGDdNMs3kz3+nmzTfrz0tKCv+vjU2Ds/+iiGwuAgAcPW7Vk5IQQloeJycnyOVy3Lx5s85AsT5eXl4wMzPDsmXL0LVrV0ilUgQGBkIul0Mmkwn9EQHAw8ND6OdXLSkpCcbGxrC3b/h8tL6+vlAqlSgsLETHjh3VNuu6KjYawMPDA1VVVTh9+rSw786dO7h27Ro8PT2FfSEhITh06BCuXr2Ko0ePIiQkRDh28uRJODk54dNPP0X37t3h6uqKnJychmVApeL77FePVrpxg/9tfSQpKQkTJkzAiBEj4OXlBWtra2RnZ6tdQiKRqA3EAYBu3bohNTVV473q2LGjMGJdR0cHAwYMwMqVK3Hp0iVkZ2fj6NGjDcv3c/L09ESbNm2ELSYmps60gwcPxrJly2rtQwsAa9asweTJkzFx4kR4enpi48aNMDQ0xPd1DcLVYtoTJAL89Dfffgts2cKvPRkeztcSVo92Cw3lm3qrnT4N7NoF/PUXcOIEEBzMf8DnzFG/rkrFB4lhYcCTw/czM4GlS/lBM9nZwN69/Ov07QvUaFpoLqyK//Lo62vXrSKEkKbi4OAAmUyGwsJCBAUF4cGDB406n+M49O3bF3FxcUJA6O3tjfLyciQkJCCgxmwWH374IfLy8jBt2jT8+eef+PXXX7Fo0SLMnDmzUVPauLm5ISQkBKGhodi1axeysrJw5swZxMTEYP/+/Y3Kf02urq4YNmwYJk+ejMTERFy8eBHvvfce7OzsMKxGBUjfvn1hbW2NkJAQuLi4qDVPu7q6Ijc3F/Hx8cjMzMQXX3yB3bt3NywD1tb8AM7q2sXbt/mp44qKAMbg6uqKXbt2CTWB7777rkYtqLOzM44fP478/HwUFfGtYHPnzsXJkycRGRmJlJQUpKen49dffxUGruzbtw9ffPEFUlJSkJOTgx9++AEqlQru7u7P/F42RmpqKu7fvy9s82vGGo1QUVGB5ORktaZ/kUiEAQMG4FRdg3C1mHZFHmPGAKtWAdHRQNeufI3eoUP8UkMAkJsLPBopBYAfvr9gAd+nYsQIvjYxMZFf7LymI0f4c//9b83XlEj444MG8X0bZ83ih/39738vqJCNw5R8s4SBPk2mTQhpvezt7SGTyVBUVPRMgWJAQACUSqUQJIpEIvTt2xccxwn9EQG+ifvAgQM4c+YMfHx8MHXqVEyaNAkLFixodJ43b96M0NBQzJo1C+7u7hg+fDjOnj0LR0fHRl/ryev6+fnhrbfeQs+ePcEYw4EDB9SaqTmOw7hx43Dx4kW1WkQA+Ne//oWPP/4YkZGR6Nq1K06ePImFCxc2PAN6evzvafXjqiq+EiUvD2vWrIGZmRl69eqFoUOHIigoCN26dVM7fcmSJcjOzkaHDh1gaWkJgA/a5XI5rl+/jj59+sDX1xfR0dGwfTRGwNTUFLt27UK/fv3g4eGBjRs3Ytu2bejcuXPj38BnYGxsDBMTE2HT09N7pusUFRVBqVTCqjpuecTKygo3b94Ung8YMACjR4/GgQMHYG9vLwSQde1vLhyrWedOnsmNGzfg4OCAvLy8RjVXNAQnvQUUW2Ho+ynY+23XJr02IaTlKysrQ1ZWFlxcXKD/ZEdDQp6XSsWPaioo4KfP0ZKFJhrjad+R5/395jgOu3fvFlYP+vvvv2FnZ4eTJ0+iZ42ucnPmzIFcLlfrRtAS0NTp2q6C/0KKOd16EhJCCCFNTCTim6AtLfnBpdX+/pvvvmVpqTnv8CvMwsICYrEYt54YhHvr1q3n6qvaXLSruZloYvyX0tCQbhUhhJBmUjNALCvjaxZzc/kp4F7gghItjUQigZ+fHxJqDMJVqVRISEhQq1lsKagmUds9msrS1IgmSiSEEKIF9PQABwcgP58fBX3tGmBuDtjbvxKT+ioUCmTUmCIvKysLKSkpMDc3h6OjI2bOnImwsDB0794dPXr0wNq1a1FcXFznmuTajIJEbfcoSLQy0461pAkhhLziOI6fUs7MjG92vn0buHuXn4Tb1pY/1oiR4i3NuXPn8MYbbwjPZ86cCQAICwtDbGwsxowZg9u3byM6Oho3b95E165dcejQIY3BLC0BBYna7lGQKKXmZkIIIdpEVxdwcuIXw8jN5aes+/tvvlaxFdcoBgYGor4xv5GRkcL0Pi0ZBYlajP8Q8h2CpVKaAocQQogWMjLip5C7c4dfzq9mgFhZyQeTpEWiIFGL3S+7D3BGABPDwFBZ/wmEEEJIc+A4vkaxpvv3+SX+bGz4EdKtuAm6taI7psUUFQpAwi967+HWeqvuCSGEtEL//MPXLP79N79qS/Vz0mJQkKjFiiuLASUfHJoY0iS5hBBCWhAnJ6B9e775uaKCXwY3PZ2fQoe0CBQkarGSyhIhSDQyoJ4BhBBCGobjOOzZs6e5M8EPYuncmW9y5jjgwQPg6lWgxhJ1RHtRkKjF7jwoAVR8h1+lkma0J4S0HhMmTBCWMqu2Y8cO6OvrY/Xq1c2TKVKv2NhYmJqaNu4ksZhfC7pzZ6BNG77J+RnXRiYvF1VPabG79yqFxwYGzZgRQgh5wTZt2oSIiAhs3LixzkmHKyoqIHkJU6u8rNfRJi+lzPr6gKsroFDwI6Kr3bvHN0m3wHWhWzuqSdRi9x5UCY9rfp8IIeRpGOOnrHvZ27OOSVi5ciWmTZuG+Ph4tQAxMDAQkZGRmDFjBiwsLBAUFKRx7pUrVyASiXD79m0AwN27dyESiTB27FghzbJly/D666/X+frOzs5YunQpQkNDYWJigilTpgAAEhMT0adPHxgYGMDBwQHTp09HcXGx2nnLli1DaGgopFIpnJycsHfvXty+fRvDhg2DVCqFt7c3zp07p/Z6O3fuROfOnaGnpwdnZ2e1mtNPPvkE/v7+Gnn08fHBkiVLAABnz57FwIEDYWFhgTZt2iAgIADnz59/6nv8pLre2zVr1sDLywtGRkZwcHDAhx9+CIWCH0Apk8kwceJE3L9/HxzHgeM4LF68GABQXl6OqKgo2NnZwcjICP7+/pDJZMLr5eTkYOjQoTAzM4ORlRU6d+mCAwcOAFVVQHY2v7xfTg7/nGgNChK1WFnZ49tDNYmEkIYqKQGk0pe/lZQ0Pq9z587F0qVLsW/fPowYMULj+JYtWyCRSJCUlISNGzdqHO/cuTPatm0LuVwOADhx4oTacwCQy+UIDAx8aj5WrVoFHx8fXLhwAQsXLkRmZiaCg4Px9ttv49KlS9i+fTsSExM1Jkj+/PPP0bt3b1y4cAFvvvkmxo8fj9DQULz33ns4f/48OnTogNDQUGHy5eTkZLzzzjsYO3YsLl++jMWLF2PhwoWIjY0FAISEhODMmTPIzMwUXuPq1au4dOkS3n33XQDAw4cPERYWhsTERPzxxx9wdXXFkCFD8LCRayjX9t6KRCJ88cUXuHr1KrZs2YKjR49izpw5AIBevXph7dq1MDExQUFBAQoKChAVFQWAnzz61KlTiI+Px6VLlzB69GgEBwcjPT0dABAREYHy8nIcP34cly9fxooVKyCVSvm/LIyN+Qzdvs2Pgr59m0ZBawtGnlteXh4DwPLy8pr0ulsP/MX4b4qqSa9LCGk9SktLWWpqKistLRX2KRTs0f8dL3dTKBqe77CwMCaRSBgAlpCQUGuagIAA5uvrW++1Ro4cySIiIhhjjM2YMYPNnj2bmZmZsbS0NFZRUcEMDQ3Z4cOH6zzfycmJDR8+XG3fpEmT2JQpU9T2nThxgolEIuG9dnJyYu+9955wvKCggAFgCxcuFPadOnWKAWAFBQWMMcbeffddNnDgQLXrzp49m3l6egrPfXx82JIlS4Tn8+fPZ/7+/nXmX6lUMmNjY/a///1P2AeA7d69u85zGvre/vLLL6xt27bC882bN7M2bdqopcnJyWFisZjl5+er7e/fvz+bP38+Y4wxLy8vtnjx4rpf6P59xq5cYezsWX67epWxhw/rzV9D1PYdqfaifr9bC6pJ1GJWei6PHtGgFUJIwxka8t2+XvbW2C5l3t7ecHZ2xqJFi4QmzSf5+fnVe52AgAChaVMul6Nfv37o27cvZDIZzp49i8rKSvTu3fup1+jevbva84sXLyI2NhZSqVTYgoKCoFKpkJWVpVaGatVr83p5eWnsKywsBACkpaVp5KV3795IT0+HUskvmhASEoKtW7cC4Ffe2rZtG0JCQoT0t27dwuTJk+Hq6oo2bdrAxMQECoUCubm59b5XNdX23h45cgT9+/eHnZ0djI2NMX78eNy5cwclT6kmvnz5MpRKJdzc3NTeL7lcLtSITp8+HcuWLUPv3r2xaNEiXLp0Sf0iJiaApyfg4MAPdCkpAf78Eygvb1SZSNOigStarLrrC0cxIiGkETiuZfRjtrOzw44dO/DGG28gODgYBw8ehHF10+MjRg0oSGBgIGbMmIH09HSkpqbi9ddfx59//gmZTIZ//vkH3bt3h2E9EeyTr6NQKPDBBx9g+vTpGmkdHR2Fx7o1lpzjHv1nXds+lUpVbzmqjRs3DnPnzsX58+dRWlqKvLw8jBkzRjgeFhaGO3fuYN26dXBycoKenh569uyJioqKBr8GoFnm7OxsvPXWWwgPD8d//vMfmJubIzExEZMmTUJFRUWd76FCoYBYLEZycjLEYvUlZKVSKQDg/fffR1BQEPbv34/Dhw8jJiYGq1evxrRp0x4n5jjAyoqfNic/n39ecxQ0Y/SD+JJRkKjFKh8NbqaVjAghrZWTkxPkcrkQKB46dEgjUKyPl5cXzMzMsGzZMnTt2hVSqRSBgYFYsWIF/vnnn3r7I9amW7duSE1NRceOHRt97tN4eHggKSlJbV9SUhLc3NyEAMve3h4BAQGIi4tDaWkpBg4ciHbt2qml/+qrrzBkyBAAQF5eHoqKip47b8nJyVCpVFi9ejVEj354fv75Z7U0EolEqPGs5uvrC6VSicLCQvTp06fO6zs4OGDq1KmYOnUq5s+fj2+//VY9SKymqws4O6v3S6ys5H8MnwhCyYtF4YcWc3bmawNcXOpNSgghLZaDgwNkMhkKCwsRFBSEBw8eNOp8juPQt29fxMXFCQGht7c3ysvLkZCQgICAgEbnae7cuTh58iQiIyORkpKC9PR0/PrrrxoDVxpr1qxZSEhIwNKlS3H9+nVs2bIFX375pTAApFpISAji4+Pxyy+/qDU1A4Crqyt+/PFHpKWl4fTp0wgJCYFBE4xu7NixIyorK7F+/Xr89ddf+PHHHzUGCzk7O0OhUCAhIQFFRUUoKSmBm5sbQkJCEBoail27diErKwtnzpxBTEwM9u/fDwCYMWMGfvvtN2RlZeH8+fM4duwYPDw8np6hmrWGjFGA2AwoSNRi3brx/XweDQ4jhJBWy97eHjKZDEVFRc8UKAYEBECpVApBokgkQt++fcFxXL39EWvj7e0NuVyO69evo0+fPvD19UV0dDRsbW0bfa2aunXrhp9//hnx8fHo0qULoqOjsWTJEkyYMEEt3ahRo4S+gE9OOv7dd9/hn3/+Qbdu3TB+/HhMnz5drabxWfn4+GDNmjVYsWIFunTpgri4OMTExKil6dWrF6ZOnYoxY8bA0tISK1euBABs3rwZoaGhmDVrFtzd3TF8+HCcPXtWaJpXKpWIiIiAh4cHgoOD4ebmhq+++qrhmXvF5q3UFhxjNM78ed24cQMODg7Iy8uDvb19c2eHEPIKKSsrQ1ZWFlxcXKCvT2u8E/Kkp31H6Pf76agmkRBCCCGEaKAgkRBCCCGEaKAgkRBCCCGEaKAgkRBCCCGEaKAgkRBCWgEag0hI7ei78ewoSCSEkBasenWPpy2bRsirrHolmidXgyH1oxVXCCGkBROLxTA1NRXWBjY0NBSWgiPkVadSqXD79m0YGhpCR4dCnsaid4wQQlo4a2trABACRULIYyKRCI6OjvTH0zOgIJEQQlo4juNgY2ODdu3aobJ60XdCCAB+venqtahJ41CQSAghrYRYLKZ+V4SQJkOhNSGEEEII0UBBIiGEEEII0UBBIiGEEEII0UB9EpuASqUCABQUFDRzTgghhBDSUNW/29W/40QdBYlN4NatWwCAHj16NHNOCCGEENJYt27dgqOjY3NnQ+twjNareW5VVVW4cOECrKysmnyY/cOHD+Hp6YnU1FQYGxs36bW1EZW3daPytm6vWnmBV6/Mra28KpUKt27dgq+vL022XQsKErXcgwcP0KZNG9y/fx8mJibNnZ0XjsrbulF5W7dXrbzAq1fmV628rzoauEIIIYQQQjRQkEgIIYQQQjRQkKjl9PT0sGjRIujp6TV3Vl4KKm/rRuVt3V618gKvXplftfK+6qhPIiGEEEII0UA1iYQQQgghRAMFiYQQQgghRAMFiYQQQgghRAMFiYQQQgghRAMFiVpsw4YNcHZ2hr6+Pvz9/XHmzJnmzlKTWLx4MTiOU9s6deokHC8rK0NERATatm0LqVSKt99+W1j6sCU4fvw4hg4dCltbW3Achz179qgdZ4whOjoaNjY2MDAwwIABA5Cenq6W5u7duwgJCYGJiQlMTU0xadIkKBSKl1iKxqmvzBMmTNC458HBwWppWkqZY2Ji8Nprr8HY2Bjt2rXD8OHDce3aNbU0DfkM5+bm4s0334ShoSHatWuH2bNno6qq6mUWpUEaUt7AwECN+zt16lS1NC2lvADw9ddfw9vbGyYmJjAxMUHPnj1x8OBB4Xhrur9A/eVtbfeXNBwFiVpq+/btmDlzJhYtWoTz58/Dx8cHQUFBKCwsbO6sNYnOnTujoKBA2BITE4VjH3/8Mf73v//hl19+gVwux99//42RI0c2Y24bp7i4GD4+PtiwYUOtx1euXIkvvvgCGzduxOnTp2FkZISgoCCUlZUJaUJCQnD16lX8/vvv2LdvH44fP44pU6a8rCI0Wn1lBoDg4GC1e75t2za14y2lzHK5HBEREfjjjz/w+++/o7KyEoMGDUJxcbGQpr7PsFKpxJtvvomKigqcPHkSW7ZsQWxsLKKjo5ujSE/VkPICwOTJk9Xu78qVK4VjLam8AGBvb4/ly5cjOTkZ586dQ79+/TBs2DBcvXoVQOu6v0D95QVa1/0ljcCIVurRoweLiIgQniuVSmZra8tiYmKaMVdNY9GiRczHx6fWY/fu3WO6urrsl19+EfalpaUxAOzUqVMvKYdNBwDbvXu38FylUjFra2v23//+V9h37949pqenx7Zt28YYYyw1NZUBYGfPnhXSHDx4kHEcx/Lz819a3p/Vk2VmjLGwsDA2bNiwOs9pyWUuLCxkAJhcLmeMNewzfODAASYSidjNmzeFNF9//TUzMTFh5eXlL7cAjfRkeRljLCAggH300Ud1ntOSy1vNzMyMbdq0qdXf32rV5WXs1bi/pHZUk6iFKioqkJycjAEDBgj7RCIRBgwYgFOnTjVjzppOeno6bG1t0b59e4SEhCA3NxcAkJycjMrKSrWyd+rUCY6Ojq2i7FlZWbh586Za+dq0aQN/f3+hfKdOnYKpqSm6d+8upBkwYABEIhFOnz790vPcVGQyGdq1awd3d3eEh4fjzp07wrGWXOb79+8DAMzNzQE07DN86tQpeHl5wcrKSkgTFBSEBw8eqNXeaKMny1stLi4OFhYW6NKlC+bPn4+SkhLhWEsur1KpRHx8PIqLi9GzZ89Wf3+fLG+11np/ydPpNHcGiKaioiIolUq1LxwAWFlZ4c8//2ymXDUdf39/xMbGwt3dHQUFBfi///s/9OnTB1euXMHNmzchkUhgamqqdo6VlRVu3rzZPBluQtVlqO3eVh+7efMm2rVrp3ZcR0cH5ubmLfY9CA4OxsiRI+Hi4oLMzEx88sknGDx4ME6dOgWxWNxiy6xSqTBjxgz07t0bXbp0AYAGfYZv3rxZ62eg+pi2qq28APDuu+/CyckJtra2uHTpEubOnYtr165h165dAFpmeS9fvoyePXuirKwMUqkUu3fvhqenJ1JSUlrl/a2rvEDrvL+kYShIJC/d4MGDhcfe3t7w9/eHk5MTfv75ZxgYGDRjzsiLMnbsWOGxl5cXvL290aFDB8hkMvTv378Zc/Z8IiIicOXKFbU+ta1ZXeWt2XfUy8sLNjY26N+/PzIzM9GhQ4eXnc0m4e7ujpSUFNy/fx87duxAWFgY5HJ5c2frhamrvJ6enq3y/pKGoeZmLWRhYQGxWKwxWu7WrVuwtrZuply9OKampnBzc0NGRgasra1RUVGBe/fuqaVpLWWvLsPT7q21tbXGAKWqqircvXu3VbwHANC+fXtYWFggIyMDQMssc2RkJPbt24djx47B3t5e2N+Qz7C1tXWtn4HqY9qorvLWxt/fHwDU7m9LK69EIkHHjh3h5+eHmJgY+Pj4YN26da32/tZV3tq0hvtLGoaCRC0kkUjg5+eHhIQEYZ9KpUJCQoJaH5HWQqFQIDMzEzY2NvDz84Ourq5a2a9du4bc3NxWUXYXFxdYW1urle/Bgwc4ffq0UL6ePXvi3r17SE5OFtIcPXoUKpVK+M+5pbtx4wbu3LkDGxsbAC2rzIwxREZGYvfu3Th69ChcXFzUjjfkM9yzZ09cvnxZLTD+/fffYWJiIjTxaYv6ylublJQUAFC7vy2lvHVRqVQoLy9vdfe3LtXlrU1rvL+kDs09cobULj4+nunp6bHY2FiWmprKpkyZwkxNTdVGj7VUs2bNYjKZjGVlZbGkpCQ2YMAAZmFhwQoLCxljjE2dOpU5Ojqyo0ePsnPnzrGePXuynj17NnOuG+7hw4fswoUL7MKFCwwAW7NmDbtw4QLLyclhjDG2fPlyZmpqyn799Vd26dIlNmzYMObi4sJKS0uFawQHBzNfX192+vRplpiYyFxdXdm4ceOaq0j1elqZHz58yKKiotipU6dYVlYWO3LkCOvWrRtzdXVlZWVlwjVaSpnDw8NZmzZtmEwmYwUFBcJWUlIipKnvM1xVVcW6dOnCBg0axFJSUtihQ4eYpaUlmz9/fnMU6anqK29GRgZbsmQJO3fuHMvKymK//vora9++Pevbt69wjZZUXsYYmzdvHpPL5SwrK4tdunSJzZs3j3Ecxw4fPswYa133l7Gnl7c13l/ScBQkarH169czR0dHJpFIWI8ePdgff/zR3FlqEmPGjGE2NjZMIpEwOzs7NmbMGJaRkSEcLy0tZR9++CEzMzNjhoaGbMSIEaygoKAZc9w4x44dYwA0trCwMMYYPw3OwoULmZWVFdPT02P9+/dn165dU7vGnTt32Lhx45hUKmUmJiZs4sSJ7OHDh81QmoZ5WplLSkrYoEGDmKWlJdPV1WVOTk5s8uTJGn/wtJQy11ZOAGzz5s1CmoZ8hrOzs9ngwYOZgYEBs7CwYLNmzWKVlZUvuTT1q6+8ubm5rG/fvszc3Jzp6emxjh07stmzZ7P79++rXaellJcxxv79738zJycnJpFImKWlJevfv78QIDLWuu4vY08vb2u8v6ThOMYYe3n1loQQQgghpCWgPomEEEIIIUQDBYmEEEIIIUQDBYmEEEIIIUQDBYmEEEIIIUQDBYmEEEIIIUQDBYmEEEIIIUQDBYmEEEIIIUQDBYmEEEIIIUQDBYmEkFZrwoQJGD58+At/ncDAQHAcB47jhHVtayOTycBxHO7du6f2nOO4l5JPQghpDAoSCSEtUnVwVde2ePFirFu3DrGxsS8lP5MnT0ZBQQG6dOnS4HN69eqFgoICvPPOOy8wZ4QQ8mx0mjsDhBDyLAoKCoTH27dvR3R0NK5duybsk0qlkEqlLy0/hoaGsLa2btQ5EokE1tbWMDAwQHl5+QvKGSGEPBuqSSSEtEjW1tbC1qZNG3Acp7ZPKpVqNDcHBgZi2rRpmDFjBszMzGBlZYVvv/0WxcXFmDhxIoyNjdGxY0ccPHhQ7bWuXLmCwYMHQyqVwsrKCuPHj0dRUVG9eTxw4ADc3NxgYGCAN954A9nZ2U38LhBCyItDQSIh5JWyZcsWWFhY4MyZM5g2bRrCw8MxevRo9OrVC+fPn8egQYMwfvx4lJSUAADu3buHfv36wdfXF+fOncOhQ4dw69atepuI8/LyMHLkSAwdOhQpKSl4//33MW/evJdRREIIaRIUJBJCXik+Pj5YsGABXF1dMX/+fOjr68PCwgKTJ0+Gq6sroqOjcefOHVy6dAkA8OWXX8LX1xefffYZOnXqBF9fX3z//fc4duwYrl+/XufrfP311+jQoQNWr14Nd3d3hISEYMKECS+plIQQ8vyoTyIh5JXi7e0tPBaLxWjbti28vLyEfVZWVgCAwsJCAMDFixdx7NixWvs3ZmZmws3NrdbXSUtLg7+/v9q+nj17Pnf+CSHkZaEgkRDyStHV1VV7znGc2j6O4wAAKpUKAKBQKDB06FCsWLFC41o2NjYvMKeEENK8KEgkhJCn6NatG3bu3AlnZ2fo6DT8v0wPDw/s3btXbd8ff/zR1NkjhJAXhvokEkLIU0RERODu3bsYN24czp49i8zMTPz222+YOHEilEplnedNnToV6enpmD17Nq5du4atW7e+tDkbCSGkKVCQSAghT2Fra4ukpCQolUoMGjQIXl5emDFjBkxNTSES1f1fqKOjI3bu3Ik9e/bAx8cHGzduxGefffYSc04IIc+HY4yx5s4EIYS0ZIGBgejatSvWrl37TOdPmDAB9+7dw549e5o0X4QQ8jyoJpEQQprAV199BalUisuXLzf4nBMnTkAqlSIuLu4F5owQQp4N1SQSQshzys/PR2lpKQC+mVkikTTovNLSUuTn5wPglxFs7LJ+hBDyIlGQSAghhBBCNFBzMyGEEEII0UBBIiGEEEII0UBBIiGEEEII0UBBIiGEEEII0UBBIiGEEEII0UBBIiGEEEII0UBBIiGEEEII0UBBIiGEEEII0fD/z3F/FA7T2scAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's convert time from sec to days \n",
    "t /= (3600 * 24)\n",
    "\n",
    "plt.figure()\n",
    "ax = plt.subplot()\n",
    "k1, = ax.plot(t, [k[0] for k in k_ref], '--', c='red', label='keff wo removal rates')\n",
    "k2, = ax.plot(t, [k[0] for k in k], c='red', label='keff w removal rates')\n",
    "ax1 = ax.twinx()\n",
    "n1, = ax1.plot(t, n_xe_ref, '--', c='green', label='Xe wo removal rates')\n",
    "n2, = ax1.plot(t, n_xe, c='green', label='Xe w removal rates')\n",
    "n3, = ax1.plot(t, n_kr_ref, '--', c='blue', label='Kr wo removal rates')\n",
    "n4, = ax1.plot(t, n_kr, c='blue', label='Kr w removal rates')\n",
    "ax.set_xlabel('Time[d]')\n",
    "ax.set_ylabel(r'$k_{eff}$', color='r')\n",
    "ax.tick_params(axis='y', colors='red')\n",
    "ax1.set_yscale('log')\n",
    "ax1.set_ylabel('Nuclides [atoms]')\n",
    "ax1.legend(handles=[k1, k2, n1, n2, n3, n4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc827041",
   "metadata": {},
   "source": [
    "# Fission products\n",
    "The removal rate for gaseous fission products has been tuned (see [here](https://info.ornl.gov/sites/publications/Files/Pub173113.pdf)) to obtain a Xenon poison fractions matching the measurements reported during the MSRE U235 operation, of 0.3%-0.4%. \n",
    "\n",
    "The Xenon poison fraction is defined as:\n",
    "$FP = \\frac{\\Sigma_a^{135}Xe}{\\Sigma_a^{235}U}$\n",
    "\n",
    "Let's plot the same quantity and see if we obtain values that matches the reference :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dc07279e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Microscopic absorption cross section at 0.0253 eV\n",
    "xs_xe135 = 2664214.0\n",
    "xs_u235 = 686.006994850397\n",
    "_, n_xe135 = results.get_atoms(str(salt.id), 'Xe135')\n",
    "_, n_u235 = results.get_atoms(str(salt.id), 'U235')\n",
    "# Poison fraction\n",
    "pf = (xs_xe135*n_xe135)/(xs_u235*n_u235)*100\n",
    "plt.figure()\n",
    "plt.plot(t, pf)\n",
    "plt.xlabel('Time [d]')\n",
    "plt.ylabel('Xe posion fraction [%]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d28beee",
   "metadata": {},
   "source": [
    "# Inventory \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "aa32c623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inventory = dict()\n",
    "for nuc,_ in openmc.data.isotopes('U'):\n",
    "    inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n",
    "\n",
    "for nuc in ['Pu238','Pu239','Pu240','Pu241','Pu242']:\n",
    "    inventory[nuc] = results.get_atoms(str(salt.id), nuc)[1] / openmc.data.AVOGADRO * openmc.data.atomic_mass(nuc) / 1000\n",
    "\n",
    "plt.figure()\n",
    "for nuc, mass in inventory.items():\n",
    "    plt.plot(t, mass, label=nuc)\n",
    "plt.xlabel('Time [y]')\n",
    "plt.ylabel('Mass [g]')\n",
    "plt.yscale('log')\n",
    "plt.ylim(1e-5)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7434b75d",
   "metadata": {},
   "source": [
    "# Neutron absorption in the fuel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0c5bd844",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import seaborn as sns\n",
    "regex = re.compile(r'(\\d+|\\s+)')\n",
    "\n",
    "# All nuclides present in the fuel at last time-step\n",
    "nucs = results.export_to_materials(-1)[0].get_nuclides()\n",
    "\n",
    "# Let's begin by making some useful groupings\n",
    "gaseos = ['H', 'He', 'Ne', 'Ar', 'Kr', 'Xe', 'Rn'] #gaseous fission products\n",
    "noble_metals = ['Se','Nb','Mo','Tc','Ru','Rh','Pd','Ag','Sb','Te'] # noble metals fission products\n",
    "metals = ['Cr','Mn','Fe','Co','Ni','Cu','Zn','Hf','Zr','W',]\n",
    "halogens = ['F','Cl','Br','I','At']\n",
    "alkali_metals = ['Li','Na','K','Rb','Cs']\n",
    "alkali_earths= ['Be','Mg','Ca','Sr','Ba','Ra']\n",
    "lanthanides  = ['Y','La','Ce','Pr','Nd','Pm','Sm','Eu','Gd','Tb','Dy','Ho','Er','Tm','Yb','Lu']\n",
    "m_a = ['Ac','Th','Pa','Np','Am','Cm','Bk','Cf','Es','Fm','Md','No','Lr']\n",
    "# Get fissile nuclides in the fuel, based on Ronen's rule for determining fissile isotopes\n",
    "fissile = []\n",
    "\n",
    "for nuc in nucs:\n",
    "    elm = regex.split(nuc)[0]\n",
    "    a = round(openmc.data.atomic_mass(nuc))\n",
    "    z = openmc.data.ATOMIC_NUMBER[elm]\n",
    "    if 90 <= z <= 100:\n",
    "        ronen = 2*z -(a-z)\n",
    "        if ronen in [41,43,45]:\n",
    "            fissile.append(nuc) \n",
    "\n",
    "# Calculate totat absorption rate of fissile nuclides\n",
    "tot_abs_rate = 0\n",
    "for nuc in fissile:\n",
    "    tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "    tot_abs_rate += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "\n",
    "nuclides_stack = dict()\n",
    "groups_stack = {'Gaseos':0, 'Noble metals':0, 'Metals':0, 'Halogens':0 , 'Alkali metals':0, 'Alkali earths':0, 'Lanthanides':0, 'MA':0, 'Others':0}\n",
    "for nuc in nucs:\n",
    "    \n",
    "    if regex.split(nuc)[0] in ['U','Pu']:\n",
    "        nuclides_stack[nuc] = results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        nuclides_stack[nuc] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "        nuclides_stack[nuc] /= tot_abs_rate\n",
    "        \n",
    "        \n",
    "    elif regex.split(nuc)[0] in gaseos:\n",
    "        groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Gaseos'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in noble_metals:\n",
    "        groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Noble metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in metals:\n",
    "        groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in halogens:\n",
    "        groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Halogens'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in alkali_metals:\n",
    "        groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Alkali metals'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in alkali_earths:\n",
    "        groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Alkali earths'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in lanthanides:\n",
    "        groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Lanthanides'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    elif regex.split(nuc)[0] in m_a:\n",
    "        groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['MA'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "    else:\n",
    "        groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, 'fission')[1]\n",
    "        groups_stack['Others'] += results.get_reaction_rate(str(salt.id), nuc, '(n,gamma)')[1]\n",
    "\n",
    "# Divide each array by the total absorption reaction rate of fissile nuclides\n",
    "for g in groups_stack.keys():\n",
    "    groups_stack[g] /= tot_abs_rate\n",
    "\n",
    "# Sort dictionary groups\n",
    "groups_stack=dict(reversed(sorted(groups_stack.items(), key=lambda item: item[1][len(item)])))\n",
    "\n",
    "# Create red color palette for groups_stack\n",
    "colors = list(reversed(sns.color_palette(\"Reds\", len(groups_stack))))\n",
    "\n",
    "# Order uranium series\n",
    "u_series = {key:value for key,value in nuclides_stack.items() if key.startswith('U')}\n",
    "u_series = dict(reversed(sorted(u_series.items(), key=lambda item: item[1][len(item)])))\n",
    "# Create green color palette for Uranium isotopes\n",
    "colors += list(reversed(sns.color_palette(\"Greens\", len(u_series))))\n",
    "\n",
    "# Order plutionium series\n",
    "pu_series = {key:value for key,value in nuclides_stack.items() if key.startswith('Pu')}\n",
    "pu_series = dict(reversed(sorted(pu_series.items(), key=lambda item: item[1][len(item)])))\n",
    "# Create blue color palette for plutonium isotopes\n",
    "colors += list(reversed(sns.color_palette(\"Blues\", len(pu_series))))\n",
    "# Add uramium and plutonium series to the stack\n",
    "groups_stack.update(u_series)\n",
    "groups_stack.update(pu_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cc4b980e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.stackplot(t, groups_stack.values(), labels=groups_stack.keys(), \n",
    "              edgecolor=\"black\", linewidth=0.5,colors=colors, alpha=0.8)\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "legend = plt.legend([handles[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n",
    "                            [labels[idx] for idx in list(reversed(np.arange(0,len(handles),1)))],\n",
    "                            bbox_to_anchor=(1.05,1), loc='upper left',\n",
    "                            borderaxespad=0, ncol=2,\n",
    "                            fontsize=15)\n",
    "plt.xlabel('Time [d]',weight='bold',fontsize=17)\n",
    "plt.title('Neutrons absorption distribution per neutron absorbed in fissile isotopes',\n",
    "                   weight='bold', fontsize=17)\n",
    "plt.xticks(fontsize=13)\n",
    "plt.yticks(fontsize=13)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c7c200",
   "metadata": {},
   "source": [
    "Here we can visualize the neutrons absorption for each nuclide present in the fuel per neutrons absorption by fissile isotopes. In other words we can see, out of the total neutrons generated, where and how many we end up losing.\n",
    "\n",
    "It is useful to group together those isotopes with similar characteristics that individually wouldn't represent a big contribution to capture.\n",
    "\n",
    "Due to the low burnup and relatively short simulation time, we are far from equilibrium. However, we can notice the quick increase of absorption in the Pu isotopes, created from neutron capture of U238, and in particular of Pu239, due to its higher absorption cross section than U235.\n",
    "\n",
    "**Note**: Neutrons lost to leakage out of the core and capture in other isotopes other than fuel are not represented. Thus, approximately 1 neutron is missing from the counting (we know that a fission event releases approximately 2.3 neutrons). This is simply due to the fact that we have defined the fuel salt as our only depletable material."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
